{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "212be17ae01eaeb0",
   "metadata": {},
   "source": [
    "### step1：导入有关数据分析和处理的库\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b38b4a0df839169",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基本库 请勿修改\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9722e58523890494",
   "metadata": {},
   "source": [
    "### step2：导入有关sklearn中有关数据集划分，数据集检验的库\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ed02b21ee79217a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T11:10:44.455560Z",
     "start_time": "2024-12-30T11:10:44.451073Z"
    }
   },
   "outputs": [],
   "source": [
    "# sklearn部分功能库 请勿修改\n",
    "# 数据集划分\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 数据归一化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 模型评估\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22246737c069cb8",
   "metadata": {},
   "source": [
    "### step3：读入数据集\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d44c1f7c-0bb1-4051-85de-ae1c4817cda5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: openpyxl in c:\\programdata\\miniconda3\\envs\\ml\\lib\\site-packages (3.1.5)\n",
      "Requirement already satisfied: et-xmlfile in c:\\programdata\\miniconda3\\envs\\ml\\lib\\site-packages (from openpyxl) (2.0.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fac7792edc710119",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T10:20:12.811787Z",
     "start_time": "2024-12-30T10:20:12.778103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>左主机转速</th>\n",
       "      <th>机舱环境温度</th>\n",
       "      <th>左主机1#缸排气温度</th>\n",
       "      <th>左主机增压器转速</th>\n",
       "      <th>左主机进口燃油流量</th>\n",
       "      <th>左主机出口燃油流量</th>\n",
       "      <th>左主机1#缸排气温度.1</th>\n",
       "      <th>左主机2#缸排气温度</th>\n",
       "      <th>左主机3#缸排气温度</th>\n",
       "      <th>左主机4#缸排气温度</th>\n",
       "      <th>左主机5#缸排气温度</th>\n",
       "      <th>左主机6#缸排气温度</th>\n",
       "      <th>左主机7#缸排气温度</th>\n",
       "      <th>左主机8#缸排气温度</th>\n",
       "      <th>左主机增压器排气温度</th>\n",
       "      <th>左主机淡水压力</th>\n",
       "      <th>左主机淡水温度</th>\n",
       "      <th>左主机海水压力</th>\n",
       "      <th>左主机滑油压力</th>\n",
       "      <th>左主机滑油温度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.481775</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.371450</td>\n",
       "      <td>0.130208</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>0.371450</td>\n",
       "      <td>0.343125</td>\n",
       "      <td>0.345062</td>\n",
       "      <td>0.347275</td>\n",
       "      <td>0.339487</td>\n",
       "      <td>0.366200</td>\n",
       "      <td>0.400250</td>\n",
       "      <td>0.394813</td>\n",
       "      <td>0.445288</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.8574</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.6623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.465275</td>\n",
       "      <td>0.4388</td>\n",
       "      <td>0.369050</td>\n",
       "      <td>0.128942</td>\n",
       "      <td>0.001217</td>\n",
       "      <td>0.001125</td>\n",
       "      <td>0.369050</td>\n",
       "      <td>0.340275</td>\n",
       "      <td>0.343162</td>\n",
       "      <td>0.344800</td>\n",
       "      <td>0.336987</td>\n",
       "      <td>0.363000</td>\n",
       "      <td>0.397688</td>\n",
       "      <td>0.391263</td>\n",
       "      <td>0.444812</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.8573</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.485350</td>\n",
       "      <td>0.4393</td>\n",
       "      <td>0.366075</td>\n",
       "      <td>0.128617</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>0.001096</td>\n",
       "      <td>0.366075</td>\n",
       "      <td>0.337713</td>\n",
       "      <td>0.341413</td>\n",
       "      <td>0.342012</td>\n",
       "      <td>0.334713</td>\n",
       "      <td>0.360063</td>\n",
       "      <td>0.395000</td>\n",
       "      <td>0.387788</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.8573</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.467175</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.363225</td>\n",
       "      <td>0.126953</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.363225</td>\n",
       "      <td>0.335375</td>\n",
       "      <td>0.340250</td>\n",
       "      <td>0.339587</td>\n",
       "      <td>0.332487</td>\n",
       "      <td>0.357525</td>\n",
       "      <td>0.392625</td>\n",
       "      <td>0.385213</td>\n",
       "      <td>0.443550</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.8573</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.6626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.474712</td>\n",
       "      <td>0.4391</td>\n",
       "      <td>0.361325</td>\n",
       "      <td>0.128400</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>0.001125</td>\n",
       "      <td>0.361325</td>\n",
       "      <td>0.333162</td>\n",
       "      <td>0.338975</td>\n",
       "      <td>0.337462</td>\n",
       "      <td>0.330587</td>\n",
       "      <td>0.355088</td>\n",
       "      <td>0.391263</td>\n",
       "      <td>0.382050</td>\n",
       "      <td>0.442975</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.8576</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.6624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9916</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9917</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9918</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9919</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9920</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9921 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         左主机转速  机舱环境温度  左主机1#缸排气温度  左主机增压器转速  左主机进口燃油流量  左主机出口燃油流量  \\\n",
       "0     0.481775  0.4390    0.371450  0.130208   0.001254   0.001096   \n",
       "1     0.465275  0.4388    0.369050  0.128942   0.001217   0.001125   \n",
       "2     0.485350  0.4393    0.366075  0.128617   0.001254   0.001096   \n",
       "3     0.467175  0.4390    0.363225  0.126953   0.001188   0.001067   \n",
       "4     0.474712  0.4391    0.361325  0.128400   0.001254   0.001125   \n",
       "...        ...     ...         ...       ...        ...        ...   \n",
       "9916  0.000000  0.4082    0.000000  0.000000   0.000000   0.000000   \n",
       "9917  0.000000  0.4085    0.000000  0.000000   0.000000   0.000000   \n",
       "9918  0.000000  0.4085    0.000000  0.000000   0.000000   0.000000   \n",
       "9919  0.000000  0.4087    0.000000  0.000000   0.000000   0.000000   \n",
       "9920  0.000000  0.4082    0.000000  0.000000   0.000000   0.000000   \n",
       "\n",
       "      左主机1#缸排气温度.1  左主机2#缸排气温度  左主机3#缸排气温度  左主机4#缸排气温度  左主机5#缸排气温度  \\\n",
       "0         0.371450    0.343125    0.345062    0.347275    0.339487   \n",
       "1         0.369050    0.340275    0.343162    0.344800    0.336987   \n",
       "2         0.366075    0.337713    0.341413    0.342012    0.334713   \n",
       "3         0.363225    0.335375    0.340250    0.339587    0.332487   \n",
       "4         0.361325    0.333162    0.338975    0.337462    0.330587   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "9916      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "9917      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "9918      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "9919      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "9920      0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "      左主机6#缸排气温度  左主机7#缸排气温度  左主机8#缸排气温度  左主机增压器排气温度  左主机淡水压力  左主机淡水温度  \\\n",
       "0       0.366200    0.400250    0.394813    0.445288     0.01   0.8574   \n",
       "1       0.363000    0.397688    0.391263    0.444812     0.01   0.8573   \n",
       "2       0.360063    0.395000    0.387788    0.444113     0.01   0.8573   \n",
       "3       0.357525    0.392625    0.385213    0.443550     0.01   0.8573   \n",
       "4       0.355088    0.391263    0.382050    0.442975     0.01   0.8576   \n",
       "...          ...         ...         ...         ...      ...      ...   \n",
       "9916    0.000000    0.000000    0.000000    0.000000     0.00   0.0000   \n",
       "9917    0.000000    0.000000    0.000000    0.000000     0.00   0.0000   \n",
       "9918    0.000000    0.000000    0.000000    0.000000     0.00   0.0000   \n",
       "9919    0.000000    0.000000    0.000000    0.000000     0.00   0.0000   \n",
       "9920    0.000000    0.000000    0.000000    0.000000     0.00   0.0000   \n",
       "\n",
       "      左主机海水压力  左主机滑油压力  左主机滑油温度  \n",
       "0        0.01    0.033   0.6623  \n",
       "1        0.01    0.033   0.6625  \n",
       "2        0.01    0.034   0.6625  \n",
       "3        0.01    0.033   0.6626  \n",
       "4        0.01    0.034   0.6624  \n",
       "...       ...      ...      ...  \n",
       "9916     0.00    0.000   0.0000  \n",
       "9917     0.00    0.000   0.0000  \n",
       "9918     0.00    0.000   0.0000  \n",
       "9919     0.00    0.000   0.0000  \n",
       "9920     0.00    0.000   0.0000  \n",
       "\n",
       "[9921 rows x 20 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('data.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704874e4a244d591",
   "metadata": {},
   "source": [
    "### step4：数据预处理\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "591f1bcc98939696",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T10:22:49.515957Z",
     "start_time": "2024-12-30T10:22:49.505901Z"
    }
   },
   "outputs": [],
   "source": [
    "X_label = data.columns[:3]\n",
    "y_label = data.columns[3:]\n",
    "X = data.iloc[:, :3].values\n",
    "Y = data.iloc[:, 3:].values\n",
    "\n",
    "# 数据集中的数据应该是已经归一化过的，所以这里不需要再进行归一化操作\n",
    "# scaler = StandardScaler()\n",
    "# X = scaler.fit_transform(X)\n",
    "# Y = scaler.fit_transform(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9e46029477d071",
   "metadata": {},
   "source": [
    "### step5：划分训练集和测试集\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9bc6a94266a9e0bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T11:12:49.781704Z",
     "start_time": "2024-12-30T11:12:49.774003Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd615b75e9c67402",
   "metadata": {},
   "source": [
    "### step6：导入你想要使用的机器学习模型\n",
    "这里以随机森林为例，你可以自由修改为你想使用的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aacaf4ae9e7f8c57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T11:03:34.009233Z",
     "start_time": "2024-12-30T11:03:33.262856Z"
    }
   },
   "outputs": [],
   "source": [
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# 支持向量机\n",
    "# from sklearn.svm import SVR\n",
    "# from sklearn.multioutput import MultiOutputRegressor\n",
    "# model = SVR()\n",
    "# model = MultiOutputRegressor(model)\n",
    "\n",
    "# 线性回归\n",
    "# from sklearn.linear_model import LinearRegression\n",
    "# model = LinearRegression()\n",
    "\n",
    "# ......更多模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3aa18477459fad7",
   "metadata": {},
   "source": [
    "### step7：训练模型\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e1f79cc44d4ac59e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T11:13:04.297290Z",
     "start_time": "2024-12-30T11:13:04.225485Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型训练\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bd1bf44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制学习曲线函数\n",
    "from sklearn.model_selection import learning_curve\n",
    "def draw_learning_curve(model, X, Y, type):\n",
    "    train_sizes, train_scores, test_scores = learning_curve(estimator=model, X=X_train, y=Y_train, train_sizes=np.linspace(0.1, 1.0, 10), cv=10, n_jobs=-1, scoring=type)\n",
    "    train_mean = np.mean(train_scores, axis=1)\n",
    "    train_std = np.std(train_scores, axis=1)\n",
    "    test_mean = np.mean(test_scores, axis=1)\n",
    "    test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(train_sizes, train_mean, label='Training score')\n",
    "    plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.2)\n",
    "    plt.plot(train_sizes, test_mean, label='Cross-validation score')\n",
    "    plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.2)\n",
    "    plt.title(f'Learning Curve({type})')\n",
    "    plt.xlabel('Training Set Size')\n",
    "    plt.ylabel(f'{type} Score')\n",
    "    plt.legend(loc='best')\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3b18f656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_learning_curve(model, X_train, Y_train, 'neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e2bd7e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nhjymiVofVzp0sX///iHblJWVmfv2Nvq8fjnObRYvXmzK6tvb6PWCBQtCKj/q+2gQ7NmzZ71/TgAAAAD+5GoPWl5enixZsiSkjL6Wz2/cuLEp2qFDBletWiUvvPCCef6iiy6Sf//73zJ27FgZNWqUTJs2Tf7zn/+YKo02HWI4YsQIGTBggBx00EEyceJEU87fruqYk5Mj5513ntlO30dD12WXXWZCmRYIUTokUYPY3/72N7nnnnvMvLMbb7zRrJ0WqYcMAAAAADwf0L7++muzppnNnr+lAUvXHVuzZo3p2bJpiX0NY7qO2YMPPiht27aVp556KlhiX51xxhmyYcMGs2aZBistkf/RRx+FFP3Qsvxa3VEXqNaqi/r6Rx99NPi8DpN8//33TdVGDW4ZGRmmTbfffnsDfCsAAAAA/MrVgHbkkUdKIBCo8nkNaZFeM2/evGr3e+mll5pLVVJTU83i2NUtkN2hQwf53//+V+37AAAAAEBd8tQcNAAAAACIZQQ0AAAAAIgSBDQAAAAAiBIENAAAAACIEgQ0AAAAAIgSBDQAAAAAiBKultkHAAAAEFucy2jZNwMRng99rOJ2aVmZlAUCUlomUlRUJIWlIpvyCiU+oVRKywJSFpDy53U7KX8sEHJb91dSViYJcXHSPDtVWuemiVcQ0AAAAFBjgfKTXz2ftk+EA/q/8hPsisfKty0/+bZfZ29T5nidXvRkO/gacwJuPW+uHfvUE3C9UebYd5n+z9xxPh6QQFlFO0tKSmXVTpHPft0k8fEJYe9tNcz5WufnsG8HH3O0Pdi+iNs591fxuohttd+rzPG5yr8wa3/OYFLxvYQ+VvH+5nF9rHz/+ry5DntdWVnl/dv7KbX3Z++7/PuveI+w9wtU/Vyk/Ve8R+i2lSWKzJ0pe+qvB7eXO04+QLyCgAYAAOCyipPyyifnzjDgDAESEhIcQUgCUloakKLSMiksKZWiEut2cYneL5Pi0jIpKrEuxWXl17q92VYfCwSfKy4JBLcvKQuYfeh93aZEr0ut581zeq339XWlFc9rL4Z5TfljkU/AG0qiyPffuNkA1KH4OL3ESVycSFxcnOkts25bj+slMT5OMlK8FXm81VoAAIC9DEDhPTchvTS6TVnlx0xYcgSkkuIS8/zS9XlSGhdvgkxRaakUlQZMKLICj4aS0vLnyqyLBp3y8GTfdgacimCzZyHI3p+7Iah+xeml/IQ8eFsqTswj3jbbxUlZSZEkJiU7Xut83jqpVxXX1hPxNX5P5/Pl+3G8v3mP8gfMvsu3CX9v536tNlivi4+37tvhIz6+4nHdXgNKvB1WzLaO7eP1+Xhru/Ltnfsxrw1/jdk2vvzaeq7i9VYgCnm9Ppeg25ffdrxvsL3xFUFKw5N9P/iaBOu2PqbvU1ZaIrOmT5WjjxkiKSnJ5fuND37m8q/RtMf++bDuVzzmNQQ0AABQr8PfnD1BoUPIKg/zssOT9XhFUCozPT1W6NEgpD1BerGDjhWGyh/T2/q8HXTKe4HsoFMpCAUDj3MbOwxVfp19uyyQKPLF5+IFeiKclBAviQlxkhRffp0QL0kJcZKo187nzXWkx+IkOSFekhOt1yUlJEiyXpv78ea5FL2dGCcpCQlm+5SkBElJKH8sUbePNyfgevKt7JN2O4jYJ9V2cLBChPV8QrzzJLz8xNtx/m2fiztPyZ0n7SUlxTLjk4/lqCGHS2JSUsU+HK91qthfXI3fo9J2Hg0I0aS4uFhSEkRy0pMlKSlJ/ICABgCABznnbFQ1JC5Sr1Ck4XCRepLMsLZiq1fIDj1WGLKGzFnhyB4+Z11rmNHrYJgp233IqQhCu3neA11CGoLsABQSbOLDwpBex4fed27nfEyvNdSY24lWCEpOLA9HiXGSrKEn3go/GoJ0Ow1FGpxMINLXJGrYsXpBrPBTHjrs8ON4zDk8zO4B8nJPhFNxvPUzlJacKElJnAIjevHTCQBALQshhBYIqFwEobreIGchAKVD5dRvG3dKIC5eCoOBxwpFBeWBqLj8th1Y7HlDFUGmfB6RIxDZvUrh2+kwPDs42XOOwnuWoj0P6dCnpAhhqCLYVL5tBZ+qtql4zA5AKY4gZHqGyh/TUJQYVyZL5s2RPgMPl7TUZBOeNGCFDEkLDzn6P9NbVM2wthgLRQBqj4AGAIia6m/hxRAiVYCr9Hh5AArdp2P7SL1Jjtvac1NQXB6I7B4hu5BCFb051tyhikAUMQQ5e5Kq2k954YaikgQpmfNZSMnpaBMp1GjgSa7i8ZDtHT1I4YHJ9PAkhPUIRQhE9vA5awid9R7hvT4Vw+Rq1ktUaftaBCIddrX2B5F9W2b5ZtgVgIZBQAMAD6kupJg1X4pLzXb5RSVSVBYX0rvjrPhWVcgJ7+VxlsWOFJ6sXhbnY+UV5Ey1N53XU2oCkDV8rfzxsjJTYU6HrGlhA33M3DbPW0PZzGvKQu8HX1vl9mEBqrxXKHIvUkVPU3T0FFUOBhoYzFyf8h4ie/iaPdcnMdIQuPJhcHYg0tfb21nbVDxuBaOKwGMFIutxa65QnKTqULny+Ubx8aa0QcQCBnZhBLsgQn0EIgDwCwIaANQhO7RULJpZsV6NDmnT9V7sdWfM7SoW1ywNOAsblNU44BSXlMiaTXGyfM5yKZP4WgUc6/HI90sdIcjsJ/h8+WNh7YmO0FN7Ghoqwkx5UYTy4BLe+1NVKDLbBV8T3sMUGpI0FCXEB2TNwq+lW9+DJS0lySqqoEPoHBXTgnOCnCWly8OOczhcpGDkfB2hCACiHwENgO+FBycTqoLhqXLYshc1tRfXLC0LDVfB/QYCsrOwRHYU2Jdi2V5+rfe3l19X3C+RvMJi0+OzdwEnQeTnJRKtBRS0OILeTnDcr7itj1thRx+z5hmVP1++jT6fWMX9impyVc1DsuYXWUPzKkJSSpLeTzDbBkthV1E4oVJosivROXqPIs0vqqrnSIfK/W+pyEGdGjNUDgBAQAMQ271UFc9VHbacoaoquq6RM2TlhQUsZ/Cyw5eGs7rsSaoIJFZhhEgBRstQl+TnSUZWTsXjlUJRxe2ksIBTKTSFbe/cV6T92cPp9HayoyiDtkuryDnXGzLRx7k+UNjaQsqscxO+5pD10krrGwWH3zmKMEQajkcBBgBANCOgAWgQ9lC+0jrupaot3UdFr5YjWEV6zHFfe7X2VFpSgmSlJpZfkkKus0NuJ0lmSqKkJSdUVKgLCzj2OkFVhZuyklL5/ovp0ntQb0lMSoy8qKr18mDvT/g25v92E5yc9yv2Q+gBAGBvEdAA7BFrHpI9X6m8NLfOVyovxGCHq9r0UtWWVt7bUc2QwUiP5RWW7HGlPA1HGqJCg1ZowLLvO4OXBizNLXbvk129rmKYXui6SHsTcnS43Pci0qZRGsPlAADwIAIagBAl5aErv7DY3N+QVygSV2IFL0cAq8vApSFOg1N4r1XI3KxKIazEVOHb216tqgJXeM+WXqcnJ1QKT/HxVlEJe9ihCWHlazM5F6nVEAYAALA7BDTAB3SIoN3DZS9Ca5cgt8uZ271fdvAqK7UWz92wvVDiE2r+nwrd5/b8YtmaX1XYKu/JctzWIYd7mvc0GIWGqorbmSlVDCMs79Xa3X6dPV3BhXDDQpjOkQIAAKgrBDTAw3T+lt2rFd7D5Rx6qD1Ue0pfu6OoWLbuKpJt+cXmsnVXsWwrKJZtel0exraVP7+zyFqHa09oD1VVPVg6NyukxyvN2kZ7wmo6JDDSMEOtFpgYHr60MAVzqQAAgAsIaEAUCu/hCq5d5Qhfeq3FNvakN21XUWlIsNJru9fLhLBdViDbvCNBdn3xZa0rEZq5WiZAVTU3K1Eyw0JYVkriHg8DtIcZOnu6wud62cMQAQAAohkBDWggGoyCiwkHe73KpDhCkY3azu/SEvAmVDkClvZwmZ6u/CJHGLOe13bUTEWg0QCVk54kOWlJkpuWJDnpyZKTqo8lW/fNY0mSUz6E0F7/aU/py4PDDMPnczHMEAAAxCgCGlDHwwxNyAqZ32XP+6p56tLApvO2NFxZIavi4gxaW/OLZHt+ieQX135YoQ4NNKFKA1d58HJeslMTZOfvC6Rn34MkJz21zopcaPByzudKMNcMMwQAAFAENKCaHi+tEmgX1QgvI1+bYYa6dpcWxQgGLMecreD98rldOtRQ1+SqLQ00FUGrvFcr3ert0uGGIb1caUmSkphQfZtLS+S3jSKN0pMlvppwZvd02ZfEkNsMMwQAAKgNAhp8TYOXCWElASksLTWLEeuleDel5DW8aa9VcL6WI2QFi2g4hhZq6NrTeVw6ZNAOWjnBoYWhj2kwq02xjCrfM16DXnmIKg+ejTOSJSU5yQwjtMOXbhOyLQAAAOoEAQ0xTYNUYYkdwqxrO4QVVdH7paFt/Y5CWbOtQNZsy5cNOwpDi2eUB649WYNLKxE6hxHaPV4hvVwawNKswhl7Oo/L7tVyBiodSpgQ5+jlCr8fNqRQFzz+UURa5qSy4DEAAEADIaDB87Snq6LnqywYyPR+VfO+tKjGuu0awvJlzdYCWbO9QNZszTehbP2Oghr3dqUmxYcGrjQrXEUaWqi3d7f2VjjNSxrSdE6WGTZoByrHfTt8acgy2+o1vVoAAACeRECDJ4pwFNnBqzyE2T1gel3VMMRdRSWy1vSCFchqDWLbCsrv58vGvKJq3zM5MV5aZadKq9xUaZ6VauZh2XO6nIEsNan6eVxOOiSworfKmpMVOmywYu5WsNeLoAUAAOArBDRExTBEnfNVFKknrMRa/6uq1+0oKAkORbSv7VCmQxF3tyhyq5xUaZWTZq5b56SZ4Xx6W+ddVTWfK3z4YHigilwsg4qEAAAA2D0CGhpMQVGp7CqWYDEOE8rKw1h1xTi27CoOHYoYvJ0vOwurLy+vvVwauFqWBzD7toYyXSQ5PDTpXe09S0mML79OCK7DZQcthg8CAACgvhDQUKfFOMKHH+r9/EJrOOHSjTslPqHyj5z2kG3KKwwORbR7wOxeMd1vdZpkJIf0hLXKTZOWOjwxJ1UyUhKrXIcrNIhVBDIAAADALQQ01JgdvuzrYHXE8jL1VdFKiZqxVm/Nl7V5xVbvV3BIYoGs215g1hSrinZYNctKqQhgjjDWIju1ynlgWkjDGbzsIKYXhhsCAAAgGhHQUKkYR0gpesd8sKqGIdoKirUyYmjvl7lszZf1OxIk8OV3Vb5Whw5q2AoJYLnWsEQNZ1VVP9Rhh87g5QxiDEUEAACA1xDQfKYowppg9tDEqkrSO+0srCjK4ayQqLc37ayuMqIVpFqXhy+7GIc9L6xJZkqVFQu1s0vL2ScnJEiKuY4PXifWsmw9AAAAEM0IaD7yw6ptu+0F07lk201lxIqy9KZE/dYCWbu9wCzWXJ0MUxnR6v0y19mp0iIrSUpWfi+9DjxUEhIjL3gcqTiHfb+2a4cBAAAAXkVA8xE7nGkI27yzqPJQxPLbu4qqr4yo64HZPWAV88Ks66wIlRHLSkvkt/UawqxetEjFObQ3jHlhAAAAiKhER2qV6ImlSKDUunbeDl6XlT9XYj2W3kQku7V4CQHNB7btKpZ/T/9FFqzaFixVr0Mbq9M0M9mqhJgbGsD0kp5c/Y9NeHGOeCmV30SkR6ssSU5OruNPBwAAgKhmB6bqglTwuTJHACsRKSmx9rFxkRYfqP177274WBQioPmABqYnP10W8phO92qeZa8JFrpIs17vrtx8bYpzFBdbwyLpIQMAAPAYDTjB8FRSdZAKD17O0LV3DRC/IaD5gK4FdslRXUwRELsnrLrKiDaKcwAAAHicM0iFhKwIQSrSsEHdDg2KgOYTY4Z1lwUrt1V6nOIcAAAAUdx7VdMgFQxdYUMJfdgD5XUENB/JTE2kOAcAAEB9CwaosN4r53DBSsErULnnit4rXyKg+UinphluNwEAAMAbQwGDAao8KIX3VAXnYUV4HbAXCGgAAADwdqhyBihnsHKGqSIt0y4im5eJ6CyO8NcBUYKABgAAgIZT42F+zp6pKnqxajO/qrS8Z6tox56VawcaCAENAAAAdbDGVfml1HE70oUhgEC1CGgAAAAIDVylxY5QZQcw+zFHINPtqBII1CkCGgAAQCwL6dEq3n2PF4ELcBUBDQAAwFPzt8KHDVbX48U6WIDXuD5D8pFHHpGOHTtKamqqDBw4UObOnVvltsXFxXL77bdLly5dzPa9e/eWjz76KGSbHTt2yJVXXikdOnSQtLQ0OeSQQ+Srr74K2WbdunVy7rnnSuvWrSU9PV2OO+44+eWXX0K2OfLII836YM7LRRddVMefHgAAiN8Dl4ar4nyRgu0iuzaL5G0Q2b5GZOsKkc1LRTb+IrL+J5G1C0TWzBdZ94PIhkUim5aIbPlNZNsKkby1Irs2ihRstYpglOTTGwZ4lKs9aK+//rqMHj1aHn/8cRPOJk6cKMOGDZPFixdL8+bNK21/4403yksvvSRPPvmkdO/eXSZPniynnHKKfP7559K3b1+zzfnnny8//PCDvPjiiyaA6fZDhgyRhQsXSps2bSQQCMjJJ58sSUlJ8s4770h2drZMmDAhuE1GRsVaYRdccIEJhDYNcwAAANWWfa+uQEb4kELKuwOIpoCmwUhD0MiRI819DWoffPCBPPPMM3LttddW2l5D1w033CDDhw839y+++GL55JNP5P777zdBLD8/X958800TvAYPHmy2ufXWW+W9996Txx57TO644w7TU/bFF1+YELfffvuZbfS5li1byquvvmoCnjOQ6eMAAMDnNHiVFomUFlo9XoX5oWtq2UMKqVAIwKsBraioSL755hu57rrrgo/Fx8ebnqw5c+ZEfE1hYaEZ2uikwxhnz55tbpeUlEhpaWm12+g+lHMbfd+UlBSzjTOgvfzyyyb4aUg74YQT5Kabbqq2F033be9fbd++PTg0Uy9+ZX92P38HXsRx8yaOm/dwzKIsgJVpCCsWKdHr8tt6HdDhghWKy9fUKs7fxppaHhE8ZvZ6aPDHcSsp1f/ASjSo6X/nXQtoGzduNGGqRYsWIY/r/UWLFkV8jQ5/1F437R3TeWhTp06Vt956y+xHZWVlyaBBg2TcuHHSo0cPsy/tFdPA17VrV7ONDo1s3769CYZPPPGEGdL4wAMPyMqVK2XNmjXB9zrrrLPMPDYdJvn999/LNddcY4Ze6vtV5a677pLbbrut0uMff/wxwyNFZMqUKW43AXuA4+ZNHDfv4Zh505TvVrvdBNQSx8xvx22liMyTaLBr164abRcX0ElZLli9erWZE6bzxzRU2caOHSszZ86UL7/8stJrNmzYYIZE6pBFLdqhIU173HRIpA5vVL/++quMGjVKZs2aJQkJCdKvXz/Zd999TW/dTz/9ZLbR2+edd5589913Zhvdh/ai6Vfx4YcfRmzvtGnT5JhjjpElS5aY961pD1q7du1MGNW5bn6lfy3QE4+hQ4eauX/wBo6bN3HcvIdjVke0WmGJowesNLwHrG7neulf8/WEcWjv1pJED5oncMx8etzSm4lkt5JooNmgadOmsm3btmqzgWs9aNo4DUdaUdFJ71c176tZs2YyadIkKSgokE2bNpneLZ2r1rlz5+A2Gp404O3cudN8Ca1atZIzzjgjZJv+/fvL/PnzzZejQy11v1qkZMCAAVW2V59X1QU0HSapl3D6Dy7/6PI9eBXHzZs4bt7DMdsNLazhnAOmt0sKaxbAzDld/ZyQ6wkjJ/vewjHz2XFLTND/wEo0qOl/41376UxOTjZBSYcp2srKysx9Z49aJDp/THvfdM6ZFgU56aSTKm2jQxc1nG3ZssVUe4y0TU5OjglnWjjk66+/jriNTQOd0n0CAIB6CGBFO0Xyt4jkrbdKzG/6VWT9IpE134msWyCycbFVVn77KpGdG0QKt1vl5KmECCCGuFrFUUvsjxgxwvRcHXTQQabMvvZ82VUdzznnHBPEdG6X0mGPq1atkj59+phrrdCooU6HRdo0jOlQxW7dupnerjFjxph5Z/Y+1RtvvGGCmc5FW7BggVxxxRWm9P6xxx4bHCb5yiuvmGqRTZo0MXPQrrrqKjP3rVevXg3+PQEA4HmRer2cF6ofAoD7AU2HHuq8sptvvlnWrl1rgpcuPG0XDlm+fLmZG2bToY26FtrSpUslMzPTBCgtvZ+bmxvcRoctagEQLfrRuHFjOe2002T8+PEhXYpaDETDoQ6n1B4xDYJaodHZu6fl++3AqPPIdD/63gAAIAJT+bDQMe8rLIgRwAAg+gOauvTSS80lkhkzZoTcP+KII8xi0tU5/fTTzaU6l19+ublURQOZzmMDAADlgmXnHRfnY+JKzTEAiDmuBzQAAOAyLegc3usVPhSRAAYADYKABgBArBffKNOgVSxSVlIRusxjjucIYAAQFQhoAAB4UakdusKCFsELADyNgAYAQLQNNQzv8ap0v4TgBQAxioAGAICbwauwwNpm3UKReCodAoDfEdAAANhTZWVVDy10hjDT41WF0vJQFtBtKpaWAQD4EwENAIBIwStYSCN8qKEjhAVK3W4pACDGENAAAP5RVlrNHK+SilDGosoAAJcQ0AAAsaE4fzeFNQheAIDoR0ADAHi7+MauzSI714uUlBfbAADAwwhoAABvDlXcuVFk5warZwwAgBhBQAMAeEdJkRXKdm2iQAcAICYR0AAA3phflrdOJH8rCzQDAGIaAQ0AEL0Kd4jkrRcp3O52SwAAaBAENABA9BX+yN9iDWUs3uV2awAAaFAENABA9CwOrXPLtCKjrkcGAIAPEdAAAO7SNcq0IuOujdaaZQAA+BgBDQDgjuICq7dM1zGj8AcAAAYBDQDQsArzrGBWsM3tlgAAEHUIaACAhqEl8rXwR1Ge2y0BACBqEdAAAPVb+MNUZFwvUlLgdmsAAIh6BDQAQN0rLbGKfmjxj7Jit1sDAIBnENAAAHWnpKi88McmkUCZ260BAMBzCGgAgL1XtMsKZjrPjIqMAADsMQIaAGDPFWwXyVsvUrTD7ZYAABATCGgAgNoJBKzCHxrMSvLdbg0AADGFgAYAqJmyUmtumZbKLy1yuzUAAMQkAhoAoHqlxVYo04qMgVK3WwMAQEwjoAEAIisuEMlbZw1npPAHAAANgoAGAAhVmGcFs8LtbrcEAADfIaABACym8McGkeKdbrcEAADfIqABgJ+VlYnkb7YqMpYWut0aAAB8j4AGAH5UWiKya6NV/KOsxO3WAACAcgQ0APCTkkKrt0x7zQJlbrcGAIDaLfdSlCdSuMOaL120I+y2/dyOivtFu0T6nCVy9A3iFQQ0APCDop1WMCvYRkVGAIB7dNSGHaScASs8WBXq7TxJLNghQ/O2SuIP+Xs+R3r7avESAhoAxDINZFr4Q//BAwCgLpQWhQarkJ4sR9gqD1kh90sKavVWcSKSHv5gUppIcqZISpZ1cd4Ov5/TTqR1H/ESAhoAxGpFxi2bav0PIQDABwIBR8gK67WKFKycwwr1ui6KSiVliKQ4Q1ZW+f3M8tvWpSQpQz5fli+DenWWpLQc6/n4WkSYjOYiOW3ESwhoABBLY/N1GKPatkIkId7tFgEA6jNkleRXMTzQOT9rR+T7ZcV72YA4R5iqHKwi3y8PZMkZNQ5ZgdIy2bJ+pUhOW9/8u0ZAAwCvKymyqjHu0h6zvf0HFwDQYAGrON/qnTKXnY7rnRXDBZ3Phc/XCpTuXRvi4qsfHhi8HaGnS0OWvh51joAGAF6l/7CbioxbKPwBAA2ttDgsPIWHrLwqgpbjubqopqs9UcEwZfdQOXqtIt4vv05KF4nTWV6IJgQ0APAa/aupBrPC7W63BAC8SYNR8S5HgLJDU6SgVcVzOoerLsQlVPRIaXAKXjLCHs+I3NOVmErIijEENADwylAY7SnToYx6UgEAfl/TMWJPlSNMhT2WWJQnQ3dsk8QfC6y1sepq5IH2QkUMVOHhKuw5+35CCgELIQhoABDNysqsuWUazOqiahYARENBI/1DU3VD/yLNv3LOzdqDAhcRy7XHJ1XupQoJVLt5TsNZfEJdfTOAQUADgGhUWlJe+GOjtagnAERrL1bB9vKiFY7rgrD7zsf3dLHhSuJEktNr3FtVkpAmny/bJYMO6CRJaeVDBBNT6qgtQN0hoAFANNFhNxrKdDhjXUweB4DdlmovCA1SNQ1ce7vOYkJyaLDabU+W/Xj5Y6bARc2rCAbLtef6p1w7vImABgBu95QFT3p21MG6NAB8Sf+gY4b/1SZo1cF/d+wy7anZ5cUrwq4rPW4XuNC5V8l1+Q0AMYOABgANTU+i7BOnOhvqAyAm6JBmM2wwQo9VpMBlP66v2Zted7tUuzNgpdYgcLEWFlDnCGgA0BBr5ZiTqG3WSRRzyoDYpyXYg71U22seuPQPOHtDS67XOmjpXKw0KgkCUYKABgD1MafDrJdTfgJWku92iwDs7e+0hqddm62qqrs2S/zOjdJz1SpJ0GrtRRGC2N7Oz0rKiBysqgtcFL0AYgIBDQDqQklR+V/B9aJDjUrdbhGA3dHe7Pyt5aHLCl6Sb4cwx2N6CZunpYXV99Eb6/dkftbuAlemNeQQgC/x2w8Ae7o+WfCv5nVQzQxA3ZZ/3xUpaG0KDWAazmqzWLGGp/TGIulNpCy1kSzdniCd2rWWhLQc5mcBqDMENACoqWJHKeq9nZAPYM+GDkcKWuFhrDbzuDRApTUyocsOXxWX8vtpet04pOpgaWmZ/PjtSunQt60kULIdQCwFtEceeUTuvfdeWbt2rfTu3VsefvhhOeiggyJuW1xcLHfddZc8//zzsmrVKunWrZv861//kuOOOy64zY4dO+Smm26St99+W9avXy99+/aVBx98UA488MDgNuvWrZNrrrlGPv74Y9m6dasMHjzYvO8++5jBCkZBQYFcffXV8tprr0lhYaEMGzZMHn30UWnRokU9fyMAokZZaegkf530D6Bu6R86gsMMnb1bmys/VpvfQQ1TzqCVVkX4Ss0RidcBiwAQHVwNaK+//rqMHj1aHn/8cRk4cKBMnDjRBKHFixdL8+bNK21/4403yksvvSRPPvmkdO/eXSZPniynnHKKfP755yaIqfPPP19++OEHefHFF6V169Zm+yFDhsjChQulTZs2EggE5OSTT5akpCR55513JDs7WyZMmBDcJiMjw+znqquukg8++EDeeOMNycnJkUsvvVROPfVU+eyzzxr8ewLQwAtFh1RTq8XwJwAVNEyFzOmqYm6XPl+b3mgdNhgpaAV7usof07lfVCUE4EGuBjQNRhdccIGMHDnS3NegpqHomWeekWuvvbbS9hq6brjhBhk+fLi5f/HFF8snn3wi999/vwli+fn58uabb5rgpb1i6tZbb5X33ntPHnvsMbnjjjvkl19+kS+++MKEuP32289so8+1bNlSXn31VRPwtm3bJk8//bS88sorcvTRR5ttnn32WenRo4d57cEHH9yA3xKAesVC0UDt/4jhDFomgEUIX/p7VWNxImm5jiGFEcKXfZ8qhQBinGsBraioSL755hu57rrrgo/Fx8ebnqw5c+ZEfI0ONUxNTQ15LC0tTWbPnm1ul5SUSGlpabXb6D6Ucxt935SUFLONBjRtlw6n1LbYtMeuffv2pm1VBTTdt71/tX279Y+T7ksvfmV/dj9/B14U08ct2EumxT20RnbsKC4tC7lG9IuKYxYsI79J4spDl7netVni8u1rK4TF1aIgTiA+yczvCpSHq0CafW0FL/O43tY5YDUdZhglP9tRcdxQKxwznx63klI9mZFoUNNzKtcC2saNG02YCp/TpfcXLVoU8TU6/FF73bR3rEuXLjJ16lR56623zH5UVlaWDBo0SMaNG2d6u3Rf2iumoapr164hQUuD4RNPPGGGND7wwAOycuVKWbNmjdlG58MlJydLbm5upbbpc1XR+XG33XZbpcd1rlt6err43ZQpU9xuAvYAx82bpny32u0mwM1jFghIYlmBJJdsl5SSHZJcskNS9Hbx9vLbjsfMZZvE12JpiJL4VClIypGCpFwpSMyVQr0uvxQmlj+elCPFCRGGGeqoYf27SPBvIxr4rH9/vYjfNe/hmPntuK0UkXkSDXbt2uWNIiG1ocU+dEikhqy4uDgT0nR4pA6JdA6DHDVqlJlvlpCQIP369ZMzzzzT9IopnXumoe68886Txo0bm220p+z4448389P2hoY+nVPn7EFr166dHHvssWaum1/pXwv0JH/o0KHm+4c3ePq46e9y8U5rPTJdKLrUPyXw9S+M+o/Y0N6tJYnKcrFzzOyf6fytElewVaRgm0iB3t4mkr/Fui5wPKfb7cFw3YAWzEhrIoHyqoYBU1yjvKdLe77sHq+kNNGBhnrJEX/id817OGY+PW7pzUSyW0k0sEfXRW1Aa9q0qQlHWlHRSe/rfLBImjVrJpMmTTIVFjdt2mSKgOhctc6dOwe30dA2c+ZM2blzp/kSWrVqJWeccUbINv3795f58+ebuWY61FL3q0VKBgwYYJ7X99fHtcKjsxeturYpHSapl3B6cuu5E9x6wPfgTZ45btUtFO3Df4j1HzFOQDxQMl6D1K4t0mrrEkn+JVESC62gZYWsLdbt/PLHdFHl2kpMtaoUauBKzRXR9br02twuv+htE7waSVyC9btOaY2a43fNezhmPjtuiQl6MiPRoKbnU64FNB1CqEFJhylqVUVVVlZm7mvFxOro/DHtIdO/8GtRkNNPP73SNjp0US9btmwx1R7vueeeSttodUalhUO+/vprMzRSabv0C9S2nHbaaeYxrSy5fPlyM4QSQBRgoWhEE61CqD+HzkClpeOd1/ZzJnhtC/4RQf8hNovLLKvB+ySlOQKWhq1G5SFLQ5gjeNnXGtAAAJ7i6hBHHQ44YsQI03Ola59pmX3t+bKrOp5zzjkmiOncLvXll1+a9c/69OljrrVCo4a6sWPHBvepYUyHKuoaaUuWLJExY8aYIZH2PpWWztdeM52LtmDBArniiitMSNShiHZw0yGQ2j4dBqnDEy+77DITzqjgCLi9UHR5xUUWika9B67tjkBVMXQwcvjatmc/j0npEkjNlS2laZLbtLnEm56uCD1eduiigiEAxDxXA5oOPdywYYPcfPPNpviGBq+PPvooWDhEe6y0wqJNhzbqWmhLly6VzMxMU25f55w5hyHqsEWdC6ZFPzRcaQ/Y+PHjQ7oUtRiIhi8dsqhDIDUI6uLWTlo4RN9bX+9cqBqAGwtFl4cyForGXv0sbQ8NVBq8zP0IPV667Z4ELl2jK7wXy9njZQcvDWF6SUyRktIy+fTblTK8X1uJZ9gVAPheXGBvK2OgSjoHTnvjNDT6vUjI//73PxOoPTGXCe4eNxaK3uvJ1P8rP9n3zRwLDVLrfhTZ+Evo3C1nj5cWi9mTnyVd7DjYqxVhCKEdtuxtyudw1YYvj1kM4Lh5D8fMp8cto7lIThvxUjbwVBVHADGIhaKxJ7RgxprvRZbNEvntU2tx5JpIyXaErUaVhxCGXOeIxPPPJACgYfEvD4CGpz1j2qOhgUxLhwM1UVossvrb8lA22+ohcw4tbNUnWI0wYo9XajaBCwAQ9fiXCkDDnFibCnfbrOIee1IuHP5UUiiy8msrlP3+mfXz4+wN63iYSKcjRNr026PhhQAARBsCGoC6L3+vxTz0oifT2lNWku92q+AlxfkiK760QtnyOdZ9m/aOdRpsXVr1pkcMABBz+JcNQO16wsylyOoFC7mtoaw4dIFooDbDXjWMLZ1lhbPSwornMpqVh7IjRFrsJxKf4GZLAQCoVwQ0AFavlxbnsEOWXorKF37euEQkXnvFtHgHFRVRh7R3VYctak+ZDmN0FojJamUFss6DRZp1F4mj4hoAwB8IaECsswOXCWBV9IBF6vUqLV8DqmSXCOWIUVe0BL4W+Fg6U2T1vNCfvZx2Ip2PsIJZk64icXFuthQAAFcQ0IBY6fVyDjN0BjJ6veC2nRtEln0qsmymyNoFoQtAN+5SMaesUUdCGQDA9whoQLSuDWZ6uYrDbjvCF5UQEc12rKkIZbqItFOzbhVzynLautVCAACiEgENaEiBgKOXq5pCG/R6wYu2rrDmk2ko2/hz6HMt9i8PZYdb88sAAEBEBDSgrmhPV3DIIb1e8MkfHLb8ZgUyDWabl1Y8p0U9WvaqCGVaiREAAOwWAQ2oTa9XdYU26PWCX34fNi2xinxoMNu2ouK5uARrwWgNZbqAtK5ZBgAAaoWABlSnuEBk4+LQogaA3+jP//qfyocvzrLml9nik0TaDrBCWYdDRVKz3WwpAACeR0ADqquSqMO3CGfwo7JSkXU/VIQyrcRoS0gRaTfQWqOs/SCR5Aw3WwoAQEwhoAFV2b5SpCTf7VYADUeH7K6ebwWy3z611iyzJaVZYUwrL7Y7yLoPAADqHAENiGTXZpFdm9xuBVD/dA7lqm/KQ9lskcLtFc8lZ1rDFnXx6Db9RRJT3GwpAAC+QEADwpUUimxb6XYrgPr9GV85V2TpLJHln4sU7ax4LjXHKvChPWWt+4okJLnZUgAAfIeABkQqGx4odbslQN0q3iWy/Eurp2z5HJGSgorn0puIdDzc6ilreYBIPP80AADgFv4VBpy2r7JOZIFYUJQn8vvnVihbMbd8OYhymS3K1ygbLNJiP2vdMgAA4DoCGmDTggjOSnWAFxVsk/abZkrC5B9EVn8Tujh6dmtr6KJemnUTiYtzs6UAACACAhpgz8nZ6lhwF/ASLWijBT6WzZTE1fOlr3NpiNwO1tBF7Slr3IVQBgBAlCOgAWbe2e/MO4O35K23SuEvnSmydoH+IJuHNX5tTWsvWT2OkYQuR4o06uB2SwEAQC0Q0IDtq0WKHVXsgGi1fY3pJTOX9T+FPtesh+klK+5wmMxcEpDhfdtKQgLzygAA8BoCGvytYJvIzvVutwKo2tblVi+ZFvrY9IvjiTiRlvtXFPrQoh+qVIc3skwEAABeRUCDf5UUWSe/QNQNuV1WHspmWss+2LTSYqs+5aHscKs8PgAAiCkENPj3JHjr76EV7gC3fhZ1YXSdR6aXNd+J7Fhd8byuSdamnxXKOhwmkpbrZmsBAEA9I6DBn3assdaIAhpaabE1VNEEsh+s64KtodskJIm0Pag8lB0ikpLlVmsBAEADI6DBn/PO8ta53Qr4RdFOkXU/VvSQaXGP0sLKgUyLfLQ8oPzSSyQ53a0WAwAAFxHQ4L/eC+adob7L39thbN0PIpuXijjXJVMp2RVhrMX+Is32FUlIdqvFAAAgihDQ4LPiC78x7wx1R4OX/kzZgUwvkXpns1o7escOEMltZxX8AAAAeyHOmqsdn1BxHWffLr+f5L0RKQQ0+MeOtcw7w94pKRTZsDi0hyz8Z0qDV5OuoYGMaosAAFRN/+3UQBUMV/EVIas0YC0fk9teJDm18nYxiIAGfyjYLpK31u1WwIvzFbWQhwYxDWQazsqKQ7dJTBVpsV/FcMXmPZk/BgDwJxOcysOT83Z1j5nH46reZ3H5v7upuSJJSeIHBDTEPuadoaZDYLW6p3O4oi7FEC6tcWjvWJMu1j8uAADEivBhgpGGDkZ6rLqghRrjrAKxb8vvlXs9AJ2LuOnX0OGKuzZV3i63Q2ggy2rFP0AAAI/MzwrvuXL2WlXxWALxwG0cAfhg3tkOt1uBaFC8yypxHwxkP4qUFIRuo/84Nesm0sIOZPtZQyoAAIiWQhj2fK0qi2M4ruFJBDTErsIdVkCDP2lvmHO44qYllcvdJ2c4wtgBVjhLTHGrxQAAv/VmhYeqYPhybhubhTBQNQIaYlNpiTW0UbTyD3wxf0znGa5zBLLtqytvl9kidLhio46UuwcA1FyNAlWkQhn0ZqHmCGiITVrcgXlnsau0SGTjL6E9ZIXbwzaKE2nSObSHLLO5Sw0GAEQNM88qafeBiiGDcAkBDbFnx7oIJ+vw/HBVnTNmF/PQuWQa0pwSUkSa96gIYy203H2mWy0GADT0ulm7690q0WHuK62lUXxSrh3eREBDbCnMs0qlw9vy1oX2jm1eVnm4qhbvMGFs//Jy9/tYfxEFAHi8+MXuhgvuZt2sqgQYWQNvIKAhtuadmXWrmHfmKWWlIluWhQaynRsqb5fTNnS4ot6n3D0AuFv0wg5NdjGLkPs1eBxAJQQ0xA4NZ+HD3hB9tLT9+kXlwxU1kP0oUrwzdBv9h7vpvo7hivuJpDd2q8UAEGMIV0A0I6AhNuStZ95ZtMrfGlpdccPPIoHS0G2S0q0QFix3310kKc2tFgNA9IqLFJjiIwSo6h5n9AEQzQho8L6inZFLqsM921aKfPeayJrvRLatqPx8etPQcveNO1MdC0DsixSgCFcAwhDQEAPzl35j3lk0rUe26AOROf+2hjLaGnVyBLL9RTJbcpIBwNsLDdt/U0prIpKcvPtgxR+hANQQAQ3exryz6FGwVWTWfSK/zbbut+4n0uvPIs33E0nNdrt1ABC5cmBV613tbg2sYq0I+JNIThtKtgOoUwQ0eFfeBpGCbW63AmrFXJEZd4vkbxaJTxI56HyRA/5s/SUZAOp1LlZixTpYIetiVfMYAEQxAhq8qWiXyPZVbrcCJYUic/9P5Ic3rfuNOoocfaNIk65utwyAVwTXvoqw7lX42lch62ERtADEJgIavId5Z9Fh068i08aVHwsR2e9UkYF/F0lMcbtlANwQPiTQGbyqGzrIfFQACEFAg/dsXS5SWuh2K/wrUCay4L8ic58UKSsWSWskcsS1Iu0Hut0yAHUhZEigo/dqd48RtACgThDQ4C07N1rFKOCOnRusuWarvrHudzhUZPAYkbRct1sGwMk5LNA5bDA8ZO2uEAYAoMG5PoD7kUcekY4dO0pqaqoMHDhQ5s6dW+W2xcXFcvvtt0uXLl3M9r1795aPPvooZJsdO3bIlVdeKR06dJC0tDQ55JBD5KuvvgrZJi8vTy699FJp27at2aZnz57y+OOPh2xz5JFHSlxcXMjloosuquNPj1opzmfemZuWzhT573lWOEtIETlstMixdxDOgPpiSrknWwu5J2eJpOaIpDcRyWguktVaJKedNe+zcReRpvuKNO8p0uIAkVZ9RFr1shZ/b9ZNpGlXkcadRHLbWxUHs1qIZDS1er+1wmpyujU0mXAGAFHB1R60119/XUaPHm3CkYaziRMnyrBhw2Tx4sXSvHnzStvfeOON8tJLL8mTTz4p3bt3l8mTJ8spp5win3/+ufTt29dsc/7558sPP/wgL774orRu3dpsP2TIEFm4cKG0adPGbKPvOW3aNPOchsOPP/5Y/vGPf5jtTzzxxOD7XXDBBSYQ2tLT0xvke0EEZWXWXCcdXoeGL8jy5SMiP39o3dcTQS0Eoid7AKoXshZWpKqCCSKl+t+1lSKNu4qkpLIgMQD4nKsBbcKECSYEjRw50tzXoPbBBx/IM888I9dee22l7TV03XDDDTJ8+HBz/+KLL5ZPPvlE7r//fhO28vPz5c0335R33nlHBg8ebLa59dZb5b333pPHHntM7rjjDvOYBroRI0aYXjJ14YUXyhNPPGF675wBTQNZy5YtG+S7wG5sWx668DEaRKOdSyRx0lMiO1Zbawf1OUuk/7kiCaz5Ax+xi11EDFnhBTHChhXWJGSZ9bSkvCeL3y0A8DvXAlpRUZF88803ct111wUfi4+PN71dc+bMifiawsJCM7TRSYcozp5tLYxbUlIipaWl1W6jdNjju+++K6NGjTK9ZjNmzJCff/5ZHnjggZDXvfzyyyb4aUg74YQT5Kabbqq2F03bpxfb9u3bg0Mz9eJX9mff4+9g12aRvE112yhUr6xUAvNelMN+fknipEwCGc2l9IjrJNCqt/W8+Ys/olFx+bGxr+FcM8sZpOIjBC5H8AqZt1XLniwtMBsIiJSVNMx/I+EKjpv3cMy8qTiGjltNP4NrAW3jxo0mTLVo0SLkcb2/aNGiiK/R4Y/a66a9YzoPberUqfLWW2+Z/aisrCwZNGiQjBs3Tnr06GH29eqrr5rA17VrxbpMDz/8sOk10zloiYmJJhjqsEm7102dddZZZh6bBrjvv/9errnmGjP0Ut+vKnfddZfcdtttlR7XIZQMjxSZMmWK201ADaQXrpf+vz8ujXcuMfdXNBok37c9R0rWZIisWel281BDU77TXk94Cf+N9CaOm/dwzLxpSgwct127dsVeFccHH3zQDInU+WdatENDmg6P1CGRzmGQ2jOm880SEhKkX79+cuaZZ5reOmdA++KLL0wvmoawWbNmySWXXGLCmPbgKQ1wtgMOOEBatWolxxxzjPz666/mfSPR3kCd3+bsQWvXrp0ce+yxkp2dLX6lfy3QX6qhQ4dKUlJS7eadbfqFkvoNJRCQuF8mS8IPD0tccb4EktLlm9bnSM+j/iTHJrheTwg1pD1nGs6G9m4tSbF23LQHLCmt/JIqkqhDApPFt/+NhKs4bt7DMfOm4hg6bvbouqgNaE2bNjUBat26dSGP6/2q5n01a9ZMJk2aJAUFBbJp0yYTqHSuWufOnYPbaHiaOXOm7Ny503wJGqzOOOOM4DY6T+3666+Xt99+W/7whz+Yx3r16iXz58+X++67LxjQwmkRE7VkyZIqA1pKSoq5hNMfJq//QNWFWn8PW37XX0uRWDvJjEYF20U+vV9k2UzrfsteUjL4Wln1S4n0ToiPvRN9H0jy+nEz1QvTRBLtQBYbYaw6/FvhTRw37+GYeVNSDBy3mrbftX+9k5OTpX///maYoq2srMzc12GK1dE5ZtpDpnPOtCjISSedVGmbjIwME862bNliqj3a29jzwXRYo5OGRX3/qmiAU7pPNACdd5a/2e1W+MOqb0XeHGWFM513c+AFIn98QCSLAjloILpsQ2quVTpeS8ZrqXgtEd+4s0h2K2sphxgPZwAARMUQRx0OqNUUBwwYIAcddJAps689X3ZVx3POOccEMZ3bpb788ktZtWqV9OnTx1xrhUYNVWPHjg3uU8NYIBCQbt26md6uMWPGmCGR9j51qOERRxxhHtfiITrEUXvcXnjhBTO/TekwxldeecVUi2zSpImZg3bVVVeZOWra24Z6Vlwgsm2F262IfaVFIl89JfL9f6z7uqbS0TeINOte/jxFJlDX4kQSUyt6xOzhiqy/BQBAdAQ0HXq4YcMGufnmm2Xt2rUmeOnC03bhkOXLl4f0dOnQRl0LbenSpZKZmWkClM45y82tWCh327ZtZi7YypUrpXHjxnLaaafJ+PHjQ7oUX3vtNbPN2WefLZs3bzYhTbexF6LW3j0t328HRp1HpvvR90Y9Y72zhrF5mcj0O0Q2/Wrd73GiyMEXWyfLQF3QiojO4Yl2GGNtLwAAortIyKWXXmoukWj5eyft+dIFp6tz+umnm0t1dI7bs88+W+XzGsi0Vw0u2L5KpCTf7VbELi39/ePbIl8+bvWg6bCyI8aKdDjE7ZbBy3RorDOEmbljuuAyYQwAAM8FNCAof4vIro1utyJ27dokMvNfIivmWvfbDbTCWXoTt1sGL4lPCg1ipnhH5eJIAABgzxDQEB1KCkW2Mu+s3vz2mcise0QKtlnV8XQ4Y8+T6eFAzSopBnvH0kUSvF1BCwCAmA5oOidMKyoCez3szsw7sxYcRx0qzheZ86jIoves+026iBx9k0ijjm63DNGmUvGOdIp3AADgglqX2deqiePGjTPVFbVQhxbsUDfddJM8/fTT9dFG+GHeWXHNVlZHLWxYJPLWheXhLE6k1xkiJz9GOPO9OCt86dBWrdzZdF+Rlr1FmvewfjYym4ukZBHOAADwSkC744475LnnnpN77rnHVDu07b///vLUU0/VdfsQ6/K3iuzc4HYrYktZqci8l0QmXWItV5DRVOQP91vDGnXIGvxVSTE5UySjmUhuB5Gm3URa9RZp1k0kt731s5GcIRK2LiQAAPDQEEddL+z//u//5JhjjgmWpVe9e/eWRYsW1XX7EPPzzpa73YrYsmOtyPQ7RdZ+b93vfKTIYaNFUrPdbhnqW3yi1TMmOkdspUiTbiLpmW63CgAA1HdA0wWiu3btGnHoY3FxcW13B1/PO/udeWd16ZcpIrMnihTvtOYQHXqFyD7DKAQS05UUHfPFEst7R81/h+eJJFFZEQAAXwS0nj17yqeffmoWd3b673//K3379q3LtiGWbV9tBQnsvcIdVjD7dap1v8V+IkfdIJLd2u2WoS4kpESopEgBXgAAYlWt/5W/+eabZcSIEaYnTXvN3nrrLVm8eLEZ+vj+++/XTysRW7TU+871brciNqz5zhrSmLfOmm/Ub4RI37Ot4W7wmLgIlRTTKNYBAIDP1Pos7qSTTpL33ntPbr/9dsnIyDCBrV+/fuaxoUOH1k8rETtKiph3VhdKi0W+eVZk/qs6XtTqLdNeM+09Q/SLSygPYanlwxPLgxnDUQEA8L1aBbSSkhK58847ZdSoUTJlypT6axVid97Ztt9Fykrcbom3acCddofIxp+t+92Giwy6VCRZC0QgOocoOoOYY74YAADA3gS0xMREU17/nHPOqc3LAIsOwyvKc7sV3g64P71rLTxdWiiSki0y+J8inQa73TIoHWJq94Qllg9PZIgiAACo7yGOWl5/5syZ0rEji92ilnTeWQLrLe3xenEz7xFZ/rl1v01/kSOvtda3gnsl7YNzxjSUpTJEEQAANHxAO/744+Xaa6+VBQsWSP/+/c08NKcTTzxx71uF2JsvhT23/AuRmf8Syd9ilVc/6EKRA06zemxQ/zR42UMT7aGKCbrWGAAAQBQEtH/84x/mesKECZWei4uLk9JS1rVC2LA8ioLs+ULeXzwmsnCSdb9RJ5GjbxRp0sXtlsXwEEXH0ER7qGI8QRgAAERxQNPS+kCN7VjLemd7YuMvViGQrb9b9/c/zeo5S2Tx4bpd6NkRxLR3DAAAwGUsloT6XUA5b63brfCWQJnI9/8R+eopq9plWmNrrlm7g9xumYfXFnMs9GwPVWShZwAAEKX26CxFi4Tcd9998tNPP5n7PXv2lDFjxsjhhx9e1+2Dl+edbSnv/UHN5K0XmXGXyOp51v2Oh1lVGlNz3W6Zh9YWKy/WYS/0rLcZoggAAGI5oL300ksycuRIOfXUU+Xyyy83j3322WemuuNzzz0nZ511Vn20E16j4ayM4iA19ut0kU/vt5Yh0FBxyKUi3f5AVcCqJCRXLtzB8E8AAODHgDZ+/HizFtpVV10VfEyDmhYNGTduHAEN1ryzoh1ut8IbinaKfPaQyC+TrfvNuluFQHLaut2yKBqimFp5vhhDFAEAQIyq9VnO0qVL5YQTTqj0uJbXv/766+uqXfCqwjwroGH31v4gMn28yI41VgXBPmeL9B9hrbHl2yGKzh6x8mBGLyIAAPCRWp8JtmvXTqZOnSpdu3YNefyTTz4xz8HHSktEtvymlS7cbkl00+If374gMu8lqyhIVkuRo64XadlLfCMhJSyI6XWy260CAADwXkC7+uqrzZDG+fPnyyGHHBKcg6bzzx588MH6aCO8QkvCM++settWWr1m660CO7LPsSKHXi6SnCmxu7ZYeU+Yc42x+AS3WwYAABAbAe3iiy+Wli1byv333y//+c9/zGM9evSQ119/XU466aT6aCO8YMc6kcLtbrciuhfsXvw/kc8fFikpsALZ4aNFuhwtMatRR6sCJUMUAQAAamyPJruccsop5gJUzDtb43YrolfBNpFZ94v8Nsu636qPNaQxs7nErMyWImmN3G4FAABA7Ae0r776SsrKymTgwIEhj3/55ZeSkJAgAwYMqMv2wQvzznRoI/POIlv5tciMu0V2bbSKfww4T6TX6bE9xC8lWyS7ldutAAAA8KRar+B6ySWXyIoVKyo9vmrVKvMcfEbDWWmR262IPiWFInMeEfnfP61wltte5ORHRfqcGdvhLD5JJLeD260AAADwTw/awoULpV+/fpUe79u3r3kOPpK3nnlnkWxeKjLtDuta9TxZ5OCLrGIZMS3OmnfGGmUAAAB7rNZnUikpKbJu3Trp3LlzyONr1qyRxEROzHy1wPL21W63Irpoyfwf3hSZ+38ipcXWHKwjxoq0HyS+kN1aJCVGq1ECAABE6xDHY489Vq677jrZtm1b8LGtW7eaRaqHDh1a1+1DNCorZb2zcDs3ivxvrDWsUcNZ+4NFTnvaP+EsNSe2i54AAAA0kFp3ed13330yePBg6dChgxnWqHRNtBYtWsiLL75YH21EtGHeWahln4rMutca7qkLMA/6h0iPE/1TXl4/M/POAAAA3Alobdq0ke+//15efvll+e677yQtLU1GjhwpZ555piQlJdVNqxC98jZYZeMhUrxL5PN/W+ubqSb7iBx9o0gjH4UVXYi6cafYLnwCAADQgPZo0lhGRoZceOGFdd8aRLeiXSLbV7ndiuiw/ierEIj5PuJEev9FZMAokQSf/ZEiu61IUprbrQAAAPDfHLSff/5Z5s6dG/LY1KlT5aijjpKDDjpI7rzzzvpoH6IF884sZSUi374g8s4lVjjLaCbyxwkiA//uv3CW1lgko4nbrQAAAPBnQLvmmmvk/fffD95ftmyZnHDCCZKcnCyDBg2Su+66SyZOnFhf7YTbti4XKS0UX9u+RuS9K0W+fsaq2Nj5KJE/PSPS2pqL6SuJaSI57dxuBQAAgH+HOH799dcyduzY4H2dg7bvvvvK5MmTzf1evXrJww8/LFdeeWX9tBTuVigs2Cq+FQiI/PKxyGcPWvPOktJFDr1SZJ+h/ikE4hSXYK13Fl/rIrAAAADYjRqfYW3cuFHatm0bvD99+nTTg2Y78sgj5bffdAgcYkpxvr/nnRXuEJl6u8iMu6xw1mJ/q3z+vsf6M5yp3HYiSbG+6DYAAECUB7TGjRubxahVWVmZ6VE7+OCDg88XFRVJQHsaEDvKyqx5Zzqcz490PbP/jRFZOt2qVqhFQE6YKJLdSnxL59zpAtwAAABwN6BpD9m4ceNkxYoVZq6ZhjR9zLZw4ULp2LFj/bQS7ti2XKSkQHzrm2dFNiwSSckSOekRkX7niMTvUeHT2JCUIZLdxu1WAAAAxLQan22OHz9ehg4dahaoTkhIkIceesiU27fpItVHH310fbUTDW3XZpH8LeJbq+eJzH/Vun34P0Wa9xBf02Cq8878OqwTAAAg2gKa9o799NNP8uOPP0qzZs2kdevWIc/fdtttIXPU4GHFBSLbVohvFWwXmT7eWlKg23CRzke43SL35XYQSUx2uxUAAAAxr1bjtRITE6V3794Rn6vqcXiM3+ed6TzKT++zKlfmtBU55FK3W+S+jBYiqdlutwIAAMAXqJONUNtXipTki28t/lBk2SyrlPzRN1ol9f0uq4XbLQAAAPANAhpC553t2iS+tW2lyOcPWbcPPE+kWXfxtbgkt1sAAADgOwQ0WPw+70xL6k8bZ1WtbN1XpPdfxN/iRBq1d7sRAAAAvkNAA/POgiX1F1sl9Y+8zlr3zM+yW4skV1RpBQAAQMOo9Vmorn9W1ePLly+vizahoW1f5e95Z+El9TObi6+l5vAdAAAARHtA2759u5x++ulm7bMWLVrIzTffLKWlpcHnN2zYIJ06daqvdqK+6FpnuzaKv0vq30lJfVtCilVSHwAAANFdZv+mm26S7777zixIvXXrVrnjjjvk22+/lbfeekuSk631kQJaohzeUVIostXH885MSf37RXZuoKS+0mGduhh1fILbLQEAAPCtGvegTZo0SZ544gn505/+JOeff758/fXXptfshBNOkMLCQrNNXFxcfbYVdR1OzLyzil5Qf5bUn2mV1D+KkvqS3VYk2effAQAAgFcCmoaxDh0qhj41bdpUPvnkE9mxY4cMHz5cdu3aVV9tRH3NOyv28TELKak/SqS5z0vqpzUWyWjidisAAAB8r8YBrX379vLTTz+FPJaVlSUff/yx5OfnyymnnLJHDXjkkUekY8eOkpqaKgMHDpS5c+dWuW1xcbHcfvvt0qVLF7N979695aOPPgrZRgPjlVdeacJkWlqaHHLIIfLVV1+FbJOXlyeXXnqptG3b1mzTs2dPefzxx0O2KSgokEsuuUSaNGkimZmZctppp8m6deskJuRvtYb1+VVZici0O6yS+q36iPTyeUn9xDSRnHZutwIAAAC1CWjHHnusPPvss5Ue1/AyefJkE5hq6/XXX5fRo0fLLbfcYuazaeAaNmyYrF+/PuL2N954oxlm+fDDD8vChQvloosuMsFw3rx5wW10+OWUKVPMXLkFCxaYdg8ZMkRWrVoV3EbfU4PdSy+9ZEKnBjoNbO+++25wm6uuukree+89eeONN2TmzJmyevVqOfXUU8XzSopEtvq82ubXWlJ/kVVS/6jr/T3nKjjvzOfLCgAAAESJGp+V3XbbbXLrrbdGfE570jQUTZs2rVZvPmHCBLngggtk5MiRwV6s9PR0eeaZZyJur6Hr+uuvN0MqO3fuLBdffLG5ff/995vntSfvzTfflHvuuUcGDx4sXbt2NW3W68ceeyy4n88//1xGjBghRx55pOm9u/DCC004tHvvtm3bJk8//bRp39FHHy39+/c34VRf98UXX4hnMe+svKT+K9btw6+mnHxue5Gk2v9xBQAAAC5XcWzUqJHpLTvmmGNMkNpnn30qhbQjjqh5ifKioiL55ptv5Lrrrgs+Fh8fb3q75syZE/E1WowkvKdOhyjOnj3b3C4pKTGl/6vbRumwR+0tGzVqlLRu3VpmzJghP//8szzwwAPmeW2XDqfUtti6d+9uhnlq2w4++OAq22cXTLGXJlC6L724bvsakYIdDf62xaVlIdeuKdwhidPvlDgJSNm+x0tph8EibrfJTWlNRBIz9Qc04tP2z2xU/Oyixjhu3sMx8yaOm/dwzLypOIaOW00/Q40DmkpKSpLvv/9e6sLGjRtNmNI11Zz0/qJFiyK+Roc/aq+W9o7pPLSpU6eaMv/2emwaEgcNGiTjxo2THj16mH29+uqrJlRpL5pNh0hqr5nOQUtMTDTB8MknnzT7VWvXrjVLB+Tm5lZqmz5Xlbvuusv0NIbTeXraM+h3U75b7d6bBwIy4LdHpM3ODZKX0kJmpJ4ipd+uFH/Tz//dbrfS3nF4D8fNezhm3sRx8x6OmTdNiYHjVtOiirUKaOqvf/2rGf539913S0N78MEHzZBI7c3Skv4a0nR4pHNIpA6D1J6xNm3aSEJCgvTr10/OPPNM0yvmDGg6VFF70bSYyKxZs0xBEO1Nc/aa1Zb2Bur8NmcPWrt27cw8uOzsbHHd2roJ17WlPWcazob2bi1JCe7MdYr7+UNJ3DpXAnEJkjrsFhnWrCKw+05cokiTriKJ1vqF1f2VR/9jOHToUPPHGXgDx817OGbexHHzHo6ZNxXH0HGzR9fVeUDTYYQaiLTEvs7NysjICHlee7hqQsv0a4AKr4yo91u2bBnxNc2aNTPrsWmFxU2bNplAde2115r5aDYNbVrUY+fOneZLaNWqlZxxxhnBbXSems5je/vtt+UPf/iDeaxXr14yf/58ue+++0xA0/fXIZi6ILezF626tqmUlBRzCac/TFHxA+VSOLJpOHMloGlJ/TkPm5txA0ZJYsue4muNO4qkhv7eVidqfn5RKxw37+GYeRPHzXs4Zt6UFAPHrabtr/XZ8g8//GB6pXQ4oc7b0gqK9kVDTk3pEEINeDpM0VZWVmbu6zDF6ugcM+0h07CoRUFOOumkSttocNRwtmXLFlNl0t7Gng+mwxqdNCzq+yttl36BzrYtXrxYli9fvtu2IZpL6vcW6e3zkvqZLURSc9xuBQAAAOqqB2369OlSV3Q4oFZTHDBggBx00EEyceJE0/OlwxbVOeecY4KYzu1SX375pSmX36dPH3OtFRo1VI0dOza4Tw1jgUBAunXrJkuWLJExY8aYIZH2PnWooRYz0ce1eIgOcdQetxdeeCHY+5eTkyPnnXeeaV/jxo3Nay677DITzqoqEIIo9fVzjpL6N/i7pH5ylkhWK7dbAQAAgLoMaHVJhx5u2LBBbr75ZlN8Q4OXrk9mFw7RHitnT5cObdS10JYuXWoqSmqJfZ1z5hyGqCXydS7YypUrTbjSBabHjx8f0qX42muvmW3OPvts2bx5swlpuo2uq2bTio763vp6rcyoBUoeffTRBvtuUAdWzxeZ/7J12+8l9eOTRBp1EImLc7slAAAAiNaApnSBaL1EouXvnbTnSxeors7pp59uLtXReWSRFt0OH0b5yCOPmAs8qHCHyPTxWr5RZN/jRTofKf4VZy1GneDtcdsAAAB+4G7VCKC+FuT+9H6RnRtEstuIHHqZ+JoOa0zJdLsVAAAAqAECGmLPzx+JLJ0hEpcgcvRNIkk+XoNOC4Jkha41CAAAgOhFQENs0ZL6nz1o3R4wSqR5d/GthBSR3A5utwIAAAC1QEBD7KCkfuV5Z36uWgkAAOBBBDTEjm/KS+onZ1JSP6etSLKPh3YCAAB4FAENsWHNdyLzykvqD/6nv0vqpzUSyWjqdisAAACwBwhoiI2S+tMoqW8kporktHe7FQAAANhDBDTEQEn9CSI714tktxY5xMcl9ePiRRp1EnEs7g4AAABv4UwO3vbLZJGl0ytK6vt53lVOO5GkVLdbAQAAgL1AQEOMlNQfKdK8h/hWelOR9MZutwIAAAB7iYAGD5fUHy9SnF9eUv9M8S1diFurNgIAAMDzCGjwcEn9n8pL6l/v35L6OrRT553FxbndEgAAANQBAhq8XVL/8KtFMluIbzXqIJKY7HYrAAAAUEcIaPBeSf3pd5aX1D9OpMtR4lsaTFNz3G4FAAAA6hABDd4rqZ+3rryk/uXiW8lZIlmt3G4FAAAA6hgBDR4sqR8vcvSN/i2pH59kDW1k3hkAAEDMIaDBG7avqiip319L6vcUf4qzwllCktsNAQAAQD0goMEjJfXvqCip3+cs8S0d1piS5XYrAAAAUE8IaIh+3zwvsl5L6mf4u6R+SrZIlo8rVgIAAPgAAQ3Rbc33IvMpqS8JySKNOrrdCgAAANQzAhqivKT+eJFAmci+w0S6HC3+nXfWyb89hwAAAD5CQEP0ltSf/YCjpP4V4ls5bf1bsRIAAMBnCGiITr98LPLrNErqpzUSyWjqdisAAADQQAhoiNKS+hOt2/3P9W9J/cRUkZz2brcCAAAADYiAhugtqd+yl0ifs8WXtOfQzDvjVxQAAMBPOPtDdPn2hYqS+kff4N/CGDntRJJS3W4FAAAAGhgBDdFVUn/eS9ZtP5fUT28qkt7Y7VYAAADABQQ0RF9J/X18XFI/Kd2q2ggAAABfIqAhukrqZ7UWOdSnJfXjEqzFqOPi3G4JAAAAXEJAg/t+mVJRUv8YH5fUz20vkpjidisAAADgIgIa3LV9NSX1VUZzkbRct1sBAAAAlxHQEAUl9XeJtDzAvyX1kzNFslu73QoAAABEAQIaXC6pv9AqqX+UT0vqxycx7wwAAABBBDS4Y62jpP5ho0WyWor/xIk06iCSkOR2QwAAABAlCGhwp6T+NEdJ/a7HiC9pKE3JcrsVAAAAiCIENLhQUn+io6T+5eJLKdk+7TUEAABAdQhocKGk/lSrpP7RN1jzz/wmIVkkt4PbrQAAAEAUIqDBvZL6LfYTf8476yiSkOh2QwAAABCFOEtEg4gLlErCzLspqZ/dxp+9hgAAAKgRetDQIPZd+47Ea0n9JB+X1E/NFcls5nYrAAAAEMUIaKh3cWsXSLe171h3DvdpSf3EVOadAQAAYLcIaKhfRXmSMPNOiZOAlHUd6s+S+loQReedxfPrBgAAgOpxxoj6NXuixOWtk53JzaV0kE9L6ue0E0lKc7sVAAAA8AACGurPLx+LLPlEAnHx8k3Hv/uzOEZ6E5H0xm63AgAAAB5BFUfUX0l9XZBaRMr6niNbAvuI7ySli2S3dbsVAAAA8BB60FD3ykpEpo+3Suq32F/KevuwpH5cAvPOAAAAUGucPaLuffuiyLofrZL6R/u0pH5ue5HEFLdbAQAAAI8hoKFurV0gMu9FR0n9VuI7Gc1F0nLdbgUAAAA8iICGulOUJzLtDpFAmYhfS+onZ4pkt3a7FQAAAPAoAhrqjhYFyVtn9ZoddqX4TnyiNe8sLs7tlgAAAMCjCGioG79MMSX1zaLMR9/oz5L6Gs4SktxuBQAAADyMgIa9t32NyOwHrNv9Roi02E98R3sNU7LcbgUAAAA8LioC2iOPPCIdO3aU1NRUGThwoMydO7fKbYuLi+X222+XLl26mO179+4tH330Ucg2O3bskCuvvFI6dOggaWlpcsghh8hXX30Vsk1cXFzEy7333hvcRtsU/vzdd99dD9+A10vq3xEsqS99fVhSPyVbJKul260AAABADHA9oL3++usyevRoueWWW+Tbb781gWvYsGGyfv36iNvfeOON8sQTT8jDDz8sCxculIsuukhOOeUUmTdvXnCb888/X6ZMmSIvvviiLFiwQI499lgZMmSIrFq1KrjNmjVrQi7PPPOMCWCnnXZayPtpGHRud9lll9Xjt+FB814KK6nvs7XPE5JFcju43QoAAADECNcD2oQJE+SCCy6QkSNHSs+ePeXxxx+X9PR0E5gi0dB1/fXXy/Dhw6Vz585y8cUXm9v333+/eT4/P1/efPNNueeee2Tw4MHStWtXufXWW831Y489FtxPy5YtQy7vvPOOHHXUUWafTllZWSHbZWT4cG5VVdb+IPLtC9btw6/yYUn9uPJ5Zz4LpQAAAKg3rp5ZFhUVyTfffCPXXXdd8LH4+HjT2zVnzpyIryksLDRDG510GOPs2bPN7ZKSEiktLa12m3Dr1q2TDz74QJ5//vlKz+mQxnHjxkn79u3lrLPOkquuukoSExOrbJtebNu3bw8Oy9SL60rL6m5fRXmSOO0OiQuUSVmXIVLa6egq919c/rh9HTOyWovEJesBllhk/8xGxc8uaozj5j0cM2/iuHkPx8ybimPouNX0M7ga0DZu3GjCVIsWLUIe1/uLFi2K+Bod/qi9bto7pvPQpk6dKm+99ZbZj93jNWjQIBOqevToYfb16quvmsCnvWiRaDDT15166qkhj19++eXSr18/ady4sXz++ecmSOowR33/SO666y657bbbKj3+8ccfm17BWNLvt8elXd5a2ZncVGZk/ElKvl2529dM+W61xJbdf+ZYoMOF4T0cN+/hmHkTx817OGbeNCUGjtuuXbtqtF1cIBAIiEtWr14tbdq0MeFHQ5Vt7NixMnPmTPnyyy8rvWbDhg1mSOR7771n5oxpSNMeNx0SqcMb1a+//iqjRo2SWbNmSUJCgglZ++67r+mt++mnnyrts3v37jJ06FAzr606+h5///vfJS8vT1JSUmrUg9auXTsTRLOzs8V1a7+vk93ELflEEmfeKYG4eCn9w0QJaHGQamjPmYazob1bS1KC66Nq915CikiTriLxCRLL9K88+h9D/d1ISmL5AK/guHkPx8ybOG7ewzHzpuIYOm6aDZo2bSrbtm2rNhu42oOmDdQApUMMnfS+zveKpFmzZjJp0iQpKCiQTZs2SevWreXaa68NmTumoU0D3s6dO80X0apVKznjjDMqzS9Tn376qSxevNgUK9kdrTCpQyh/++036datW6XnNbRFCm76wxQVP1B1EY60pP7nD5qbcf3OkcTWvWr8Ug1nng9ous5b0y4iSaFDaGNZ1Pz8olY4bt7DMfMmjpv3cMy8KSkGjltN2+/q2XJycrL079/fDFO0lZWVmfvOHrVIdI6Z9r5pYNKiICeddFKlbbSgh4azLVu2yOTJkyNu8/TTT5s2aPXI3Zk/f76ZI9e8eXPxb0n98SLFO8tL6v9VfCennUhSmtutAAAAQIxyvfycltgfMWKEDBgwQA466CCZOHGi6fnSqo7qnHPOMUFM53cpHfao5fL79OljrrVCo4Y6HRZp0zCmIze1l2vJkiUyZswYM4zR3qdNe9feeOONYAVIJ52zpu+llR11fpre1wIhf/3rX6VRo0bi35L6P1gl9Y/yYUn99CYi6Y3dbgUAAABimOtn2Dr0UOeV3XzzzbJ27VoTvHThabtwyPLly02vlU2HNupaaEuXLpXMzExTYl9L7+fm5ga30XGdWtBj5cqVpsCHrm02fvz4St2Kr732mglyZ555ZqV26VBFfV4DoM4r69SpkwloGijF7yX1D7tSJNtnJfUT00Sy27rdCgAAAMQ41wOauvTSS80lkhkzZoTcP+KII8wC1dU5/fTTzWV3LrzwQnOJRAuLfPHFF7vdhy8U7bSGNgbKRLoOEdlnqPhKXIJI4066BoTbLQEAAECM44wTuzd7osiONSJZLa3eM7/JbSeSWLn4CwAAAFDXCGio3pJPRJZMsaoX6ryz5EzxlbTGImk+nXMIAACABkdAQ9W01+zTB6zbff8m0vIA8ZWEZJEc5p0BAACg4RDQUHVJ/WmOkvr9/ia+k9s+5hejBgAAQHQhoCGyeS+Xl9RP92dJ/YxmIilZbrcCAAAAPkNAQ2XrfhT59nkfl9RPFclq7XYrAAAA4EMENFQuqT/tDkdJ/WPFX+LKhzbyqwEAAICGx1koQn32oL9L6me2EEnOcLsVAAAA8CkCGiosmSryy8f+Lamv8+00mAIAAAAuIaDBUVJ/gn9L6ttDG+Pi3G4IAAAAfIyAhrCS+vv5s6R+dmuRpDS3WwEAAACfI6BBZP4rjpL6N/qvpL4O5cxs7nYrAAAAAAKa72lJ/W+e829JfZ1vp0MbAQAAgChAQPMzZ0n9LseIdB0qvpPdViQxxe1WAAAAAAYBzc8+e8gqDqKl5Q+/yn8FMlKyRTKauN0KAAAAIIiA5uuS+pOtIX5H3+i/kvo6z46hjQAAAIgyBDQ/2rFWZLafS+rr0MY2IglJbrcCAAAACEFA82NJ/enjrflnzXv6s6R+aq5IemO3WwEAAABUQkDzY0n9tQuskvpH+7CkfnySSE47t1sBAAAARERA82tJ/UO1pH5r8Z3cdiIJPgulAAAA8AwCml8UbBeZNr6ipP4+Piypn95EJDXH7VYAAAAAVSKg+cWHY0V2rLZK6uuC1H4rqZ+QYq15BgAAAEQxApofrPhK5LtXy0vq3yCSkiW+oyX14/lxBwAAQHRjMo4ftDtQ5E/Piiz/QqRlL/GdjOYiKT5b5w0AAACeREDzi/1PFWncSXwnMc2fxVAAAADgSYz5QgyLs4Y2+m2+HQAAADyLgIbYldVSJDnd7VYAAAAANUZAQ2xKyrAqVgIAAAAeQkBD7NFqlQxtBAAAgAcR0BB7slqLJKW63QoAAACg1ghoiC3JWSKZzdxuBQAAALBHCGiIHXEJ1tBGAAAAwKMIaIgdOW1FEpPdbgUAAACwxwhoiA2pOSLpjd1uBQAAALBXCGjwvvhEkRyGNgIAAMD7CGjwvpx2IgmJbrcCAAAA2GsENHhbWmORtFy3WwEAAADUCQIavCsh2SoMAgAAAMQIAhq8S0vqxye43QoAAACgzhDQ4E0ZzURSstxuBQAAAFCnCGjwnsRUkazWbrcCAAAAqHMENHhMXPnQRn50AQAAEHs4y4W3ZLYQSc5wuxUAAABAvSCgwTuS0kWyWrrdCgAAAKDeENDgraGNcXFuNwQAAACoNwQ0eENWK5GkNLdbAQAAANQrAhqiX3KmSFYLt1sBAAAA1DsCGqJbXLw1tBEAAADwAQIaolt2G5HEFLdbAQAAADQIAhqiV0q2SEZTt1sBAAAANBgCGqJTXAJDGwEAAOA7URHQHnnkEenYsaOkpqbKwIEDZe7cuVVuW1xcLLfffrt06dLFbN+7d2/56KOPQrbZsWOHXHnlldKhQwdJS0uTQw45RL766quQbeLi4iJe7r333uA2mzdvlrPPPluys7MlNzdXzjvvPMnLy6uHbwCV5LQVSUhyuxUAAACAvwLa66+/LqNHj5ZbbrlFvv32WxO4hg0bJuvXr4+4/Y033ihPPPGEPPzww7Jw4UK56KKL5JRTTpF58+YFtzn//PNlypQp8uKLL8qCBQvk2GOPlSFDhsiqVauC26xZsybk8swzz5iAdtpppwW30XD2448/mn29//77MmvWLLnwwgvr+RuBpOaKpDd2uxUAAACA/wLahAkT5IILLpCRI0dKz5495fHHH5f09HQTmCLR0HX99dfL8OHDpXPnznLxxReb2/fff795Pj8/X95880255557ZPDgwdK1a1e59dZbzfVjjz0W3E/Lli1DLu+8844cddRRZp/qp59+Mj1zTz31lOnVO+yww0wofO2112T16tUN9O34UHySSE47t1sBAAAAuCJRXFRUVCTffPONXHfddcHH4uPjTW/XnDlzIr6msLDQDG100mGMs2fPNrdLSkqktLS02m3CrVu3Tj744AN5/vnng4/p++uwxgEDBgQf03Zp+7788kvTaxepbXqxbd++PTgsUy+uKy1z5W2Ly9/Xvq5WdiuRsoBIWRR8Xz5n/8xGxc8uaozj5j0cM2/iuHkPx8ybimPouNX0M7ga0DZu3GjCVIsWoYsQ6/1FixZFfI0Of9ReN+0d03loU6dOlbfeesvsR2VlZcmgQYNk3Lhx0qNHD7OvV1991QQu7UWLRIOZvu7UU08NPrZ27Vpp3rx5yHaJiYnSuHFj81wkd911l9x2222VHv/4449Nr6DfTfmuJj2PKxugJagNHeIL7+G4eQ/HzJs4bt7DMfOmKTFw3Hbt2hX9AW1PPPjgg2ZIZPfu3c2cMQ1pOjzSOSRSh0GOGjVK2rRpIwkJCdKvXz8588wzTW9dJPpanW8W3utWW9oTqPPpnD1o7dq1M3PgtNCI69Z+78rbas+ZhrOhvVtLUkIVo2rjk0Wa7qtdqA3dPFTzVx79j+HQoUMlKYmCLV7BcfMejpk3cdy8h2PmTcUxdNzs0XVRHdCaNm1qApQOMXTS+zovLJJmzZrJpEmTpKCgQDZt2iStW7eWa6+9Njh3TGlomzlzpuzcudN8Ea1atZIzzjgjZBvbp59+KosXLzbFSpz0/cMLlejwSa3sWFXbUlJSzCWc/jBFxQ9UVeGogWg4qzKgNemkX2BDNwk1EDU/v6gVjpv3cMy8iePmPRwzb0qKgeNW0/a7esaenJws/fv3N8MUbWVlZea+DlOsjvZ2aQ+ZhiYtCnLSSSdV2iYjI8OEsy1btsjkyZMjbvP000+bNmj1SCd9/61bt4b0uk2bNs20T4uGoA5lNBdJyXS7FQAAAIDrXB/iqEMCR4wYYYpxHHTQQTJx4kTT86XDFtU555xjgpjO71JaoEPL5ffp08dca4VGDU1jx44N7lPDWCAQkG7dusmSJUtkzJgxZkikvU+b9q698cYbwQqQTjp/7bjjjjPDKbWypHavXnrppfKXv/zF9NqhjiSmiWTzfQIAAABREdB06OGGDRvk5ptvNsU3NHhpeXu7cMjy5ctN5USbDm3UtdCWLl0qmZmZpsS+zjnTiou2bdu2mflgK1euNEU9dG2z8ePHV+pW1JL5GuR0flokL7/8sgllxxxzjGmD7uehhx6qt+/Cf+JEctvrquFuNwQAAACICq4HNKUhSC+RzJgxI+T+EUccYRaors7pp59uLruji05Xt/C0hrtXXnllt/vBHspqKZJMdUsAAADARsk8uCMpQyQzdHkFAAAAwO8IaGh4cfEMbQQAAAAiIKCh4WW1FknauzXnAAAAgFhEQEPDSs4SyWzmdisAAACAqERAQ8OJS7CGNgIAAACIiICGhqPrnSUmu90KAAAAIGoR0NBw0hq53QIAAAAgqhHQUP/iomK5PQAAACDqEdBQ/3LauN0CAAAAwBMIaKhfaY1FUnPcbgUAAADgCQQ01J+EZJGctm63AgAAAPAMAhrqT047kfgEt1sBAAAAeAYBDfUjvalIarbbrQAAAAA8hYCGupeQIpJNYRAAAACgtghoqGNxIo06iMTzowUAAADUFmfRqFuZzUWSM9xuBQAAAOBJBDTUnaR0kaxWbrcCAAAA8CwCGupInEhue5G4OLcbAgAAAHgWAQ11Q3vOktLcbgUAAADgaQQ07L3kTJGsFm63AgAAAPC8RLcbAI+Li7eGNgIAAF8oLS2V4uJi8Rptc2JiohQUFJjPAG8o9tBxS0pKkoSEhL3eDwENe0fXO0tMcbsVAACgngUCAVm7dq1s3bpVvNr+li1byooVKySOOfOeEfDYccvNzTXt3Zu2EtCw51KyRTKaut0KAADQAOxw1rx5c0lPT/fEybJTWVmZ5OXlSWZmpsSzXqtnlHnkuGmQ3LVrl6xfv97cb9VqzyubE9CwZ+ISGNoIAIBP6NAyO5w1adJEvHqiX1RUJKmpqVF9og/vHre0NKtgnoY0/V3Z0+GO0f0pEb1y2ookJLndCgAA0ADsOWfacwagavbvyN7M0ySgofZSc0XSG7vdCgAA0MC8NqwR8OLvCAENtROfJJLTzu1WAAAAADGJgIbayW0nksDURQAA4E8dO3aUiRMn1nj7GTNmmF4Vr1a/RMMjoKHm0puIpOa43QoAAIDd0lDkvGjBhkaNGplrvX/rrbfu0X6/+uorufDCC2u8/SGHHCJr1qyRnBzOoVAzdIWgZhKSrTXPAAAAPEBDke3111+Xm2++WebOnStZWVmmGqCWbXeWSNdKlbog8u40a9asVu1ITk4262LFGi2CoQszo+7Rg4aaye0gEr/3K6MDAIDYYNZ9Kipp8Iu+b01oKLIv2nulvWYtWrQw9xctWmSC2ocffij9+/eXlJQUmT17tvz6669y0kknme00wB144IHyySefVDvEUff71FNPySmnnGIq+O2zzz7y7rvvVjnE8bnnnjOLGU+ePFl69Ohh3ue4444LCZQlJSVy+eWXm+10WYNrrrlGRowYISeffHKVn/f333+XE044wfQSZmRkyH777Sf/+9//gs//+OOP8sc//lGys7PNZz/88MPN57VL2d9+++3Stm1b81306dNHPvroo+Brf/vtN/MZNOgeccQRpuT9yy+/bJ7Tz66fQx/r3r27PProozU6PqgaPWjYvYzmIikVf2UCAADILy6VnjdPbvD3XXj7MElPrptT2GuvvVbuu+8+6dy5swk2K1askOHDh8v48eNNUHnhhRdM6Fm8eLG0b1/1+q+33Xab3HPPPXLvvffKww8/LGeffbYJTI0bR656rQsa6/u++OKLpjfvr3/9q/zzn/8Mhp5//etf5vazzz5rws+DDz4okyZNkqOOOqrKNlxyySVmvbBZs2aZgLZw4cJgL+GqVatk8ODBcuSRR8q0adNMSPvss89MEFS6//vvv1+eeOIJ6du3rzzzzDNy4oknmlCngdP5fel2uo0d0rRn8t///rd5bN68eXLBBReY99dAiT1DQEP1ElNFsvZ8JXQAAIBopb1GQ4cODd7XQNW7d+/g/XHjxsnbb79tesQuvfTSKvdz7rnnyplnnmlu33nnnfLQQw+Z4ZTaM1bV8MDHH39cunTpYu7rvrUtNg151113nemVUxqAnL1hkSxfvlxOO+00OeCAA8x9DZ22Rx55xPQivvbaa8Fhifvuu2/weQ2L2kv3l7/8JRgQp0+fbnoK9bW2K6+8Uk499dTg/VtuucUENvuxTp06mWCoQY+AtucIaKhGXPnQRkbCAgCAUGlJCaY3y433rSsDBgwIuZ+Xl2eKh3zwwQdmyKH2MOXn55vwU51evXoFb2vvkfZQrV+/vsrtdSikHc5Uq1atgttv27ZN1q1bJwcddFDweS1sokMxdShiVXRI5MUXXywff/yxDBkyxIQ1u13z5883QxojzRnbvn27rF69Wg499NCQx/X+d999V+X3tXPnTjNE8rzzzjO9Zjb9ziiIsncIaKhaVkuRZGs1dAAAACedk1RXQw3domHKSYcZTpkyxfQode3aVdLS0uRPf/qTGTpYnfDgo99NdWEq0vY1nVtXlfPPP1+GDRtmwqWGtLvuusv0bl122WXmc9T196VhVj355JMycODAkO00UGLP0TWCyJLSRTJbuN0KAACABqPzsnS4og4t1KGCWlBEC2Q0JO190iIlWs7fphUmv/32292+tl27dnLRRRfJW2+9JVdffbUJT0p70j799FMztDKc9va1bt3afHYnvd+zZ88q30vbqK9bunSpCbPOiw51xJ7z9p89UD/i4q2hjXFxbrcEAACgwWhBDA03WhhEe7VuuummanvC6ov2emkPmIYdrYyoc9K2bNli2lQVnR92/PHHm7lluq3OIdMCI/YcN92HzjHTuW0aAr/44gszjLJbt24yZswYM59Mh11qBUctTqLDIu2iJdUVR9Ghlbo/nW9XWFgoX3/9tXn/0aNH1/n34hcENFSW1VokKdXtVgAAADSoCRMmyKhRo8zi0k2bNjWFM3SOVkPT9127dq2cc845ZrigLoytwxerGzqovWxayXHlypWmV0wD0wMPPGCe01L9Wr1Rg5iWydf9aBCz551pyNK5b9rrpnPhtOdMC6M4KzhWNaxS59Np9Urdtw6B1J5HDYvYc3GBvR3wiirpL7T+RUF/4PUXxXWr5+1+m+QskaZd6/RttTtdKw9p2VoWNPQOjps3cdy8h2PmTX47bgUFBbJs2TIzdE3Lq3uR9oTpuZmek2lpe6+1XXvDTj/9dFNZ0k/KPHbcqvtdqWk2oAcNFeISRHKrXuMDAAAA9U/XUNNCH9rbpcMGtcy+nvSfddZZbjcNDSD6YygaTnYbkcRkt1sBAADga9pT9Nxzz8mBBx5ohiEuWLBAPvnkk+CcMsQ2etBgSc0RyWjidisAAAB8T6sxhldVhH/QgwaR+ESRnHZutwIAAADwPQIarHCWEPsTnAEAAIBoR0Dzu7RGImm5brcCAAAAAAHN5xKSGdoIAAAARBECmp9pOIuvesFDAAAAAA2LgOZX6U1FUqNg8WwAAAAAQQQ0P0pIsdY8AwAAQEzQddNycyvqCtx6663Sp0+fal9z7rnnysknn7zX711X+4GFgOY7cSKNOugKiG43BAAAoN6tXbtWLrvsMunatau0aNFCOnToICeccIJMnTpVYtk///nPOv+Mv/32m8TFxcn8+fNDHn/wwQdNQETdYKFqv8lsLpKc4XYrAAAA6p0GikMPPdT0LP3rX/+STp06SUpKikyZMkUuueQSWbRoUcTXFRcXS1KSt5cgyszMNJeGkJOTI7GmqKhIkpOTXXlvulH8JCldJKuV260AAACxIBAQKdrZ8Bd93xr6xz/+YXp85s6dK6eddprpRdtvv/1k9OjR8sUXXwS3020ee+wxOfHEEyUjI0PGjx9vHtfHunTpYk7Uu3XrJi+++KLj4wfMMML27dub0Ne6dWu5/PLLg88/+uijss8++0hqaqrpufvTn/4UsY1lZWXStm1b815O8+bNk/j4ePn999/N/QkTJsgBBxxg2teuXTvz2fLy8qr87OFDHEtLS83n1rDapEkTGTt2rPkMTh999JEcdthhwW3++Mc/yq+//hp8XgOu6tu3r/nOjjzyyIhDHAsLC8130bx5c/P5dZ9fffVV8PkZM2aY12sP34ABAyQ9PV0OOeQQWbx4cbWB6dJLL5VWrVqZfWpP6F133RV8fuvWrfL3v//dfNf6/P777y/vv/9+8Pk333zTHHs9Vh07dpT7778/ZP/62Lhx4+Scc86R7OxsufDCC83js2fPlsMPP1zS0tLM966fa+fOnRLTAe2RRx4xX4h+kQMHDjS/QFXRv2bcfvvt5hdFt+/du7f5QXLasWOHXHnlleag6RepB9v5A2H76aefzC+hJn79QT/wwANl+fLlwef1B05/cJyXiy66SDwtt73+F8jtVgAAgFhQvEvkztYNf9H3rYHNmzeb80TtKdNzvXDO+Vp2oDnllFNkwYIFMmrUKHn77bfliiuukKuvvlp++OEHc/I/cuRImT59evCE/4EHHpAnnnhCfvnlF5k0aZIJUOrrr782J/J63qqhQ9sxePDgiO3UEHbmmWfKK6+8EvL4yy+/bHr/9JzW3u6hhx6SH3/8UZ5//nmZNm2aCVk1pYFEhyE+88wzJnTo96Of0UmDh4Y4bb+GJ31P/U40RCr7PP2TTz6RNWvWyFtvvRXxvbRd+v1oO7/99lsTjIcNG2be0+mGG24w7dL3S0xMNN97VR5++GF599135T//+Y/5TvX70QyhtH3HH3+8fPbZZ/LSSy/JwoUL5e6775aEBKta+TfffCOnn366/OUvfzHHV4/1TTfdVGlY5n333WfyhYZjfV7D6XHHHWfC/ffffy+vv/66+e40KMbsEEf9kPpD8Pjjj5twNnHiRHPw9EvXxB3uxhtvNF/6k08+Kd27d5fJkyebH5rPP//cJHl1/vnnm18i/QuH/iVDtx8yZIg5UG3aWIUx9MvWJH/eeefJbbfdZlKy/rBr6HO64IILzC+WTdO9pyWlud0CAACABrFkyRLTQ6TnjDVx1llnmQBm09CkPUPaU6XsXjc9iT/qqKPMH/ZbtmxpzjN1OKT2pB100EFmW31OQ6H2QGVlZZmQZZ+rRnL22WeboKKv0/1o4HjttdfMua9NOyBsGkzuuOMO03mgPXU1oefZ1113nZx66qnmvp5/67m0kwYRJw1zzZo1M+fR2iOlt5X2rulnj0RDnvYGavjR0KT03F2HlT799NMyZsyY4LbaU3nEEUeY29dee6384Q9/kIKCgkrn5Eq/G+2R1HN47Tixg6sdGDU8agfMvvvuax7r3Llz8HntfTzmmGNM6FK6jX6me++91xxj29FHH20CuU1zhR4b+7vX99eQrG3WzxipnZ4PaPplaQiyfxn0B+WDDz4wPwx6kMJp6NKkPXz4cHP/4osvNgdEf6A1iOXn55u0/s477wT/SqEJ+b333jNfov4gK3sf99xzT3Df2isXTgNZVT98AAAA4vepE9evdud9ayB8+N7u6FA7Jz3Zt4e52bRHSwtiqD//+c8m9GgQ0F4WPbfU4iPaEzR06FATIOzn9KKdCnpuqT0/2htn+/DDD80Quh49epheND0Hnjlzpqxfv968h03PeXVIn86b2759u5SUlJgws2vXrt12Imzbts30eGmHiE3bqZ/Z+T1pT+DNN98sX375pWzcuDHYc6bhSANaTWhHiI560+/KpgFWw6t+p069evUK3tahi2r9+vUmpIYbMWKE6cjRoab6fWr4PfbYY81zWrREh4na4Sycvu9JJ50U8pi2T4+fDv20e9rCfwa+++4703Omx8ym35d+L8uWLTPHLKYCmo4j1e5GTfI27UbVv0LMmTMn4mt0PGt4UtVhjNrVqPQHVb/k6rbRL1RDoHa96kHWLkwdT6vtCC8PqgdDg5+GNP2F09Rd3S+Atk8vNv3lUfpDqhe/sj+7n78DL+K4eRPHzXs4Zt7kt+Omn9M+MbVP2o1EF0bnaKCoQfjSP75rT4t9cm4HEftzhNPzxfDHwz+vvQ99TEdm6b41OOlFe9q0R0aHQGrvmQ7b07lW2nOkoUc7DTT4aLDQYX823Y/uT3vwNKDpOaqeg+p5aqNGjcxzWuxEX6c9ZjpPqnHjxubcVjs67B4nu532tbOtzufCP4/z+9DzXQ1HOmxTR6Lp4xqi9D1qup/abKPBKLy9JSUlEb9z7YHU8KeBVodf6pBF7RV74403guf+kY6rcz/O553t1J8Tpef5zm10jp+GdK0CGs7u6Qynj+l76e+MHfxsNf3vhWsBTVO5himdyOek96uqqKM/qNrrpr1j+kunB0fHvup+lHYhDxo0yPzgaqLVfb366qsm8OnYVzuV65et41K1R00r+ui4YO3u1V8ou5tVf0n0Lx/6w6nJ+ZprrjFDL6saa6v0rxo6ZDLcxx9/7P3hkXVA/wMF7+G4eRPHzXs4Zt7kl+OmvS36B2s9h9I/snulzTpkTesdaO+LPQ9N6xXYvUrO6oM6Esv+47o9nE17srTny6b39XHndnruqBctLqG9RDoMUucxKb2vFx0ip8MStZNAQ5BzKo/9h3y7M2DWrFny3//+15zz2u+jYUxP/DXoaYeG0tBmfx59TEOUBgP7NdppoOfIel8DiB4/3bddOESDkIZIbatuo/PD9FxX31drMyi708T+buyOCL3t/A60/bo/fUyHQWpRFQ2tdg+gPq81ITRg6jba6+dsu7ILb+Tl5YXs22YfNx02aV+08IoWUdFcsHLlyuB8t3D6vH525371vF8ft99Xv1/9Dp3baK+hzlmLNPVKt9VLOP390O9L30+/Eyf7c8dUmX3tUta/FOhYYv1B0y9Vh0fqkEjnMEidYKh/jdDU2q9fPzOGWHvrlJ109S8pV111lbmtP6g6j02HWNoBzdmlrRM+tdtVU7om90jDIZX2wun4ZJseYK32ot2vOs/Nr/SXUv8B0+5+r5es9ROOmzdx3LyHY+ZNfjtueiK6YsUKU7a9vubd1Ac9t9Phg3oupj1YOuRQq/jpH/n1Oa1B4OxBc56v6R/ntaiEhhUd4aUVAXXajP7hXbfTOVYagHTYoP4hXqfY6D569uxpTs51CJy+t/aC/e9//zPnoHrOWdU5oYYBLW6nYU63PeOMM8z+7HNR/Zl74YUXTE+aFsOwC1xoB4XuU4+Lnh/b+9fPqefC9n0teKI9fPo+ei6tBU70XFWDrG6jx1bnlmkvnoYcHdZ4yy23hHw3+jntkWk61FDfU0Ou/g7Y+9GLBjH9vvV8XHua9H01tGgvo70fZ9uVHaAzMzNDviMNnRrOnnrqKXM+rj1pGur0O9XQqefa2qmiHTiaC3SOoLZfO3z0+9DhkHos9Tjp/DHtedPgqfv797//HXwv3ad+Hud767QoPSZ6rbUrtI06d03DpxYtqep3Rb8jbU/470qk4BlRwCWFhYWBhISEwNtvvx3y+DnnnBM48cQTq31tfn5+YOXKlYGysrLA2LFjAz179qy0TV5eXmD16tXm9umnnx4YPnx48H0TExMD48aNC9le93PIIYdU+Z66P/26Pvrooxp/xm3btpnX6LWfFRUVBSZNmmSu4R0cN2/iuHkPx8yb/Hbc9Nxr4cKF5tpr9HzwkksuCXTo0CGQnJwcaNOmjTnXnD59enAbPV8LPydVjz76aKBz586BpKSkwL777ht44YUXgs/p9gMHDgxkZ2cHMjIyAgcffHDgk08+Mc99+umngSOOOCLQqFGjQFpaWqBXr16B119/fbdt1ffTtuj5cLgJEyYEWrVqZfY3bNgw0xbddsuWLeb5Z599NpCTkxPc/pZbbgn07t07eL+4uDhwxRVXmPbm5uYGRo8ebd7npJNOCm4zZcqUQI8ePQIpKSmmzTNmzKj03Tz55JOBdu3aBeLj481nVCNGjAjZj/6cXHbZZYGmTZuafR166KGBuXPnBp/X797ZdjVv3jzz2LJly0I+d2lpqdnu8ccfD/Tp08d81/oZjjnmmMC3334b3G7Tpk2BkSNHBpo0aRJITU0N7L///oH3338/+Px///tfkxn0WLZv3z5w7733hryP/nw88MADlb53bffQoUMDmZmZ5r31exk/fvwe/a7UNBvE6f8Tl2iS1W5fO4HqXws0ZWvpykhFQsLpXxJ0KKMm4TvvvDPiNlu2bDFzzLQgiN0rpklYe8Gca1lo97Wm3fASpzb9S4VWjdHJgs4JjdXRlKx/VdAudL/3oOlfOXTyrB/+yhgrOG7exHHzHo6ZN/ntuGmvgPYI6TmVl3rQnPQ8U8/N9JzMHlaH6FfmseNW3e9KTbOBq0McdTigjgnWiika1LSSio4Dtas66lhe7Rq1F6HTiZWrVq0y3cN6rV2netCca0BouVDNnNrtquVVtZSnduM6y6bqY9ptrF2PWiZV56Bpl7VO5FQ6jFGDmv5HV7t6dQ6aDofU7WsazgAAAACgtlwNaBqSNmzYYCY8rl271gQvDUt24RAd++pMyppIdT2IpUuXmvGpGqC0F8y50KAmUp0LphMFtcKNruegayw4/7qlvWU67liDny4iqGFOy/NrD5myJzbagVHHtup+nGtRAAAAAEBdc71IiA5nrGo1brtHy6YFPHRiXnV0uKNedkcLiVS1WrkGMq3SAwAAAAANKfoHcgIAAACATxDQAAAAUCMu1pYDfPM7QkADAABAtey5/DVdaBfwq13lvyN7U93V9TloAAAAiG664LEWZVu/fr25rwsN6yLAXqKVv4uKikzROS+Ua4e3jpv2nGk4098R/V3R35k9RUADAADAbrVs2dJc2yHNa/QEOj8/36x767Vw6WcBjx03DWf278qeIqABAABgt/TkuFWrVtK8eXOzULfXaJtnzZpl1rX1w+LisaLYQ8dN27c3PWc2AhoAAABqTE9A6+IktKFpm0tKSiQ1NTXqT/Th7+MWvQM5AQAAAMBnCGgAAAAAECUIaAAAAAAQJZiD1gAL1W3fvl38PrlTy47q9+CXscOxgOPmTRw37+GYeRPHzXs4Zt5UHEPHzc4Eu1vMmoBWj3bs2GGu27Vr53ZTAAAAAERJRsjJyany+bjA7iIc9mphvdWrV0tWVpYn1m2oz78WaEhdsWKFZGdnu90c1BDHzZs4bt7DMfMmjpv3cMy8aXsMHTeNXRrOWrduXe2i2/Sg1SP94tu2bet2M6KG/lJ5/RfLjzhu3sRx8x6OmTdx3LyHY+ZN2TFy3KrrObNRJAQAAAAAogQBDQAAAACiBAEN9S4lJUVuueUWcw3v4Lh5E8fNezhm3sRx8x6OmTel+PC4USQEAAAAAKIEPWgAAAAAECUIaAAAAAAQJQhoAAAAABAlCGgAAAAAECUIaKiRWbNmyQknnGBWPo+Li5NJkyaFPK+1Zm6++WZp1aqVpKWlyZAhQ+SXX34J2Wbz5s1y9tlnm0UGc3Nz5bzzzpO8vLyQbb7//ns5/PDDJTU11awaf8899zTI54tFd911lxx44IGSlZUlzZs3l5NPPlkWL14csk1BQYFccskl0qRJE8nMzJTTTjtN1q1bF7LN8uXL5Q9/+IOkp6eb/YwZM0ZKSkpCtpkxY4b069fPVFjq2rWrPPfccw3yGWPRY489Jr169QouyDlo0CD58MMPg89zzKLf3Xffbf47eeWVVwYf47hFn1tvvdUcJ+ele/fuwec5ZtFr1apV8te//tUcGz3nOOCAA+Trr78OPs85SfTp2LFjpd83vejvmOL3LYxWcQR253//+1/ghhtuCLz11lta9TPw9ttvhzx/9913B3JycgKTJk0KfPfdd4ETTzwx0KlTp0B+fn5wm+OOOy7Qu3fvwBdffBH49NNPA127dg2ceeaZwee3bdsWaNGiReDss88O/PDDD4FXX301kJaWFnjiiSca9LPGimHDhgWeffZZ813Onz8/MHz48ED79u0DeXl5wW0uuuiiQLt27QJTp04NfP3114GDDz44cMghhwSfLykpCey///6BIUOGBObNm2d+Dpo2bRq47rrrgtssXbo0kJ6eHhg9enRg4cKFgYcffjiQkJAQ+Oijjxr8M8eCd999N/DBBx8Efv7558DixYsD119/fSApKckcR8Uxi25z584NdOzYMdCrV6/AFVdcEXyc4xZ9brnllsB+++0XWLNmTfCyYcOG4PMcs+i0efPmQIcOHQLnnntu4MsvvzTf8eTJkwNLliwJbsM5SfRZv359yO/alClTzPnk9OnTzfP8voUioKHWwgNaWVlZoGXLloF77703+NjWrVsDKSkp5j9oSn9R9HVfffVVcJsPP/wwEBcXF1i1apW5/+ijjwYaNWoUKCwsDG5zzTXXBLp169ZAnyz2/+Oox2DmzJnBY6Qn/m+88UZwm59++slsM2fOHHNf/wMYHx8fWLt2bXCbxx57LJCdnR08TmPHjjUnOU5nnHGGCYioG/p78dRTT3HMotyOHTsC++yzjznxOOKII4IBjeMWvQFNT9Aj4ZhFLz0vOOyww6p8nnMSb9D/Pnbp0sUcL37fKmOII/basmXLZO3atWYIgS0nJ0cGDhwoc+bMMff1WocQDBgwILiNbh8fHy9ffvllcJvBgwdLcnJycJthw4aZYXlbtmxp0M8Ui7Zt22auGzdubK6/+eYbKS4uDjluOrynffv2IcdNh460aNEi5Jhs375dfvzxx+A2zn3Y29j7wJ4rLS2V1157TXbu3GmGOnLMopsOz9HhN+HfLccteumwNx2637lzZzPcTYdQKY5Z9Hr33XfNucSf//xnM8ytb9++8uSTTwaf55wk+hUVFclLL70ko0aNMsMc+X2rjICGvab/IVTOXxr7vv2cXut/SJ0SExNNWHBuE2kfzvfAnikrKzPzYQ499FDZf//9g9+p/sOj/0hVd9x2d0yq2kb/o5mfn1+vnytWLViwwIzB1zH0F110kbz99tvSs2dPjlkU0yD97bffmrmf4Thu0UlP2HV+ykcffWTmfuqJvc432rFjB8csii1dutQcr3322UcmT54sF198sVx++eXy/PPPm+c5J4l+Wsdg69atcu6555r7/L5VlhjhMQAx+Jf9H374QWbPnu12U1AD3bp1k/nz55tez//+978yYsQImTlzptvNQhVWrFghV1xxhUyZMsUUE4A3HH/88cHbWphHA1uHDh3kP//5jyksgej9g6P2fN15553mvvag6b9vjz/+uPlvJaLf008/bX7/tPcakdGDhr3WsmVLcx1ebUfv28/p9fr160Oe18o7WkXJuU2kfTjfA7V36aWXyvvvvy/Tp0+Xtm3bBh/X71SHGehfsao7brs7JlVto5WxOMnZM/qXRK0+1b9/f9Mj07t3b3nwwQc5ZlFKh+fof9+0cpj+FV4vGqgfeughc1v/gstxi3761/t9991XlixZwu9aFNPKjDqiwKlHjx7B4amck0S333//XT755BM5//zzg4/x+1YZAQ17rVOnTuaXYurUqcHHtDtZx3HrvBml1/qLpycytmnTppm/hOlfLe1ttJy/jkO26V+ktTehUaNGDfqZYoHWc9FwpsPj9LvW4+SkJ/9JSUkhx03H1us/cs7jpsPtnP+Q6THR/9jZ/0DqNs592NvY+8De09+TwsJCjlmUOuaYY8x3rr2e9kX/wq9zmuzbHLfopyXWf/31VxMA+F2LXjpUP3zJmJ9//tn0firOSaLbs88+a4aX6nxdG79vEUQoHAJErE6mZU31oj82EyZMMLd///33YEnb3NzcwDvvvBP4/vvvAyeddFLEkrZ9+/Y1ZXFnz55tqp05S9pqFR8tafu3v/3NlLR97bXXTLlUStrumYsvvtiUGZ4xY0ZIadtdu3YFt9Gytlp6f9q0aaas7aBBg8wlvKztsccea0r1a6naZs2aRSxrO2bMGFN16ZFHHvFsWdtocO2115pKm8uWLTO/S3pfK4t9/PHH5nmOmTc4qzgqjlv0ufrqq81/H/V37bPPPjPlu7Vst1a8VRyz6F3KIjExMTB+/PjAL7/8Enj55ZfNd/zSSy8Ft+GcJDqVlpaa3ymthhmO37dQBDTUiK5TocEs/DJixAjzvJZJvemmm8x/zLSU7THHHGPWcHLatGmT+Y9fZmamKYs6cuRIE/ycdL0SLZ+r+2jTpo35jyz2TKTjpRddG82m/1j94x//MKWE9T9qp5xyiglxTr/99lvg+OOPN+u/6MmLntQUFxdX+vno06dPIDk5OdC5c+eQ90DtjBo1yqzxo9+l/uOjv0t2OFMcM28GNI5b9NHy261atTLfpf57o/eda2lxzKLXe++9Z07W9Vyhe/fugf/7v/8LeZ5zkuik69XpeUj4sVD8voWK0/8XqWcNAAAAANCwmIMGAAAAAFGCgAYAAAAAUYKABgAAAABRgoAGAAAAAFGCgAYAAAAAUYKABgAAAABRgoAGAAAAAFGCgAYAAAAAUYKABgCISR07dpSJEyfWePsZM2ZIXFycbN26Vfzk1ltvlT59+rjdDABAOQIaAMBVGoqqu2iA2BNfffWVXHjhhTXe/pBDDpE1a9ZITk6O1Lcnn3xSevfuLZmZmZKbmyt9+/aVu+66q8av/+2338x3M3/+/N1u+/bbb8vBBx9sPldWVpbst99+cuWVVwaf/+c//ylTp07d488CAKhbiXW8PwAAakVDke3111+Xm2++WRYvXhx8TEOMLRAISGlpqSQm7v6fr2bNmtWqHcnJydKyZUupb88884wJSA899JAcccQRUlhYKN9//7388MMPdf5eGrzOOOMMGT9+vJx44okm1C1cuFCmTJkS8v06v2MAgLvoQQMAuEpDkX3RXh4NEfb9RYsWmV6fDz/8UPr37y8pKSkye/Zs+fXXX+Wkk06SFi1amHBx4IEHyieffFLtEEfd71NPPSWnnHKKpKenyz777CPvvvtulUMcn3vuOdO7NXnyZOnRo4d5n+OOOy4kUJaUlMjll19utmvSpIlcc801MmLECDn55JOr/Lz6nqeffrqcd9550rVrV9OjdeaZZ5oQ5aRt1fdNTU2V7t27y6OPPhp8rlOnTuZae960zUceeWTE93rvvffk0EMPlTFjxki3bt1k3333NW175JFHqhziGKkXU79LmwbJ448/3nwf+v3/7W9/k40bN1b5eQEAtUNAAwBEvWuvvVbuvvtu+emnn6RXr16Sl5cnw4cPNz1E8+bNM8HphBNOkOXLl1e7n9tuu82EI+2x0tefffbZsnnz5iq337Vrl9x3333y4osvyqxZs8z+dUig7V//+pe8/PLL8uyzz8pnn30m27dvl0mTJlXbBg2eX3zxhfz+++9VbqP71J5EDW36me+880656aab5PnnnzfPz50711xrKNXA+NZbb1X5Xj/++GOteud0f/ZlyZIlJkQOHjzYPKfh9eijjzbB8Ouvv5aPPvpI1q1bZ75TAEAdCQAAECWeffbZQE5OTvD+9OnTA/pP1aRJk3b72v322y/w8MMPB+936NAh8MADDwTv635uvPHG4P28vDzz2IcffhjyXlu2bAm2Re8vWbIk+JpHHnkk0KJFi+B9vX3vvfcG75eUlATat28fOOmkk6ps5+rVqwMHH3yw2fe+++4bGDFiROD1118PlJaWBrfp0qVL4JVXXgl53bhx4wKDBg0yt5ctW2ZeP2/evGq/E/2Mw4cPN9vq93HGGWcEnn766UBBQUFwm1tuuSXQu3fvSq8tKysLnHLKKYH+/fsHdu3aFWzDscceG7LdihUrzP4XL15cbVsAADVDDxoAIOoNGDAg5L72oGlPlg4B1OGFOtxOe5p214OmvW+2jIwMyc7OlvXr11e5vQ6F7NKlS/B+q1atgttv27bN9B4ddNBBwecTEhLMUMzq6D7mzJkjCxYskCuuuMIMk9RhkdoLWFZWJjt37jRDOHUIpD0/TC933HGHebw29DN+8MEHpifsxhtvNPu5+uqrTZu1d7A6119/vWnnO++8I2lpaeax7777TqZPnx7SLh1+qWrbNgBAZBQJAQBEPQ0aThrOtNCFDj/UIXgaIP70pz9JUVFRtftJSkoKua/zqzQU1WZ7qzNu7+2///7m8o9//EMuuugiOfzww2XmzJnSs2fPYKXHgQMHhrxGA+Ce0JCpl/PPP19uuOEGMxdNC7KMHDky4vYvvfSSPPDAA2ZeXps2bUKCsQ4l1aGdkYInAGDvEdAAAJ6j873OPfdcU/DDDg5aer4haUETLZKh5fztOVpaYfLbb7+t9bpidijT3jPdZ+vWrWXp0qVmjlxVFSft96stLfihPYP6XpFor5kGuSeeeMKU53fq16+fvPnmm2YfNamkCQCoPf7rCgDwHK3AqIUxtDdHe7W0gEZ1PWH15bLLLjPrl2kvng71e/jhh2XLli2mTVW5+OKLTQDTYhtt27Y1xTh0+KIuCzBo0KBgMROtDqkhUIc+ail+Lcqh+x49erQ0b97c9BpqkQ7dh1Z6jLR+m1Zo1KGMWhClQ4cOpsiHlvcvLi6WoUOHVtp+7dq1JvT+5S9/kWHDhpn7ds+dtu+SSy4xPXtadXLs2LHSuHFjM3zytddeM1Un97SHDwBQgTloAADPmTBhgjRq1MgsLq0hTcOE9u40NC2rr2HlnHPOMeFK52RpWzQwVWXIkCGmiuOf//xnM9TwtNNOM9trRUot1a+0B0sDj1aHPOCAA8x6aVr23y6vr71XGrS0l0vDni45EIm+TnvitH0aILU8voaujz/+2JTdD6fLGui8Oq0WqUMW7YsuY6D0vbT3Unvujj32WNM2XdNN5wHGx3NKAQB1IU4rhdTJngAA8DntxdPCJVp2fty4cW43BwDgQQxxBABgD+laZtobpT1VOgzx3//+tyxbtkzOOusst5sGAPAoxiMAALCHdFifDj3UIYCHHnqoKZ2vi0drLxoAAHuCIY4AAAAAECXoQQMAAACAKEFAAwAAAIAoQUADAAAAgChBQAMAAACAKEFAAwAAAIAoQUADAAAAgChBQAMAAACAKEFAAwAAAACJDv8P7Gs/FbYFmq0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_learning_curve(model, X_train, Y_train, 'r2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6544bb9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_learning_curve(model, X_train, Y_train, 'neg_mean_absolute_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7817c45e23e1a1a",
   "metadata": {},
   "source": [
    "### step8：模型评估\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6aa90236749de9ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-30T11:15:56.581426Z",
     "start_time": "2024-12-30T11:15:56.564468Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.8174354790892455e-06\n",
      "R2: 0.9985080545288421\n",
      "MAE: 0.00019133288247288814\n",
      "RMSE: 0.0016785218137067048\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "# 计算MSE\n",
    "mse = mean_squared_error(Y_test, Y_pred)\n",
    "print(f\"MSE: {mse}\")\n",
    "# 计算R2\n",
    "r2 = r2_score(Y_test, Y_pred)\n",
    "print(f\"R2: {r2}\")\n",
    "# 计算MAE\n",
    "mae = mean_absolute_error(Y_test, Y_pred)\n",
    "print(f\"MAE: {mae}\")\n",
    "# 计算RMSE\n",
    "rmse = math.sqrt(mse)\n",
    "print(f\"RMSE: {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef925867",
   "metadata": {},
   "source": [
    "### step9：模型保存\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fc367776",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'skl2onnx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[53], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mskl2onnx\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mskl2onnx\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m convert_sklearn\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mskl2onnx\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_types\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FloatTensorType\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'skl2onnx'"
     ]
    }
   ],
   "source": [
    "import skl2onnx\n",
    "from skl2onnx import convert_sklearn\n",
    "from skl2onnx.common.data_types import FloatTensorType\n",
    "import onnx\n",
    "\n",
    "# 将模型转换为 ONNX 格式\n",
    "onnx_model = convert_sklearn(model, initial_types=[('input', FloatTensorType([None, X.shape[1]]))])\n",
    "\n",
    "# 保存 ONNX 模型到文件\n",
    "onnx.save_model(onnx_model, 'model.onnx')\n",
    "\n",
    "print(\"Model saved as ONNX format!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dfa787",
   "metadata": {},
   "source": [
    "### step10：模型加载\n",
    "**请勿修改此部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e74572b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ONNX model is valid.\n"
     ]
    }
   ],
   "source": [
    "import onnx\n",
    "\n",
    "# 加载导出的 ONNX 模型\n",
    "onnx_model = onnx.load(\"model.onnx\")\n",
    "\n",
    "# 验证模型\n",
    "onnx.checker.check_model(onnx_model)\n",
    "\n",
    "# 如果没有异常抛出，说明模型是有效的\n",
    "print(\"ONNX model is valid.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "883109b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference result: [array([[1.9164883e-02, 2.0611999e-04, 2.2357998e-04, 1.9457570e-01,\n",
      "        1.7032412e-01, 2.0951919e-01, 1.8995480e-01, 1.8292083e-01,\n",
      "        1.9306913e-01, 2.4268521e-01, 2.0904805e-01, 3.7309369e-01,\n",
      "        3.8666688e-03, 8.4116721e-01, 7.9999998e-04, 1.2420000e-02,\n",
      "        6.3618320e-01]], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "import onnxruntime as ort\n",
    "import numpy as np\n",
    "\n",
    "# 加载 ONNX 模型\n",
    "onnx_session = ort.InferenceSession(\"model.onnx\")\n",
    "\n",
    "# 准备输入数据\n",
    "# input_data = X.astype(np.float32)  # 示例数据，形状应与训练时输入数据一致\n",
    "input_data = np.array([[0.0005375, 0.4387, 0.1929875]]).astype(np.float32)\n",
    "\n",
    "# 获取模型的输入和输出名称\n",
    "input_name = onnx_session.get_inputs()[0].name\n",
    "output_name = onnx_session.get_outputs()[0].name\n",
    "\n",
    "# 进行推理\n",
    "result = onnx_session.run([output_name], {input_name: input_data})\n",
    "\n",
    "# 打印结果\n",
    "print(\"Inference result:\", result)"
   ]
  }
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